Complete Fibre Pushforwards, Parameter Families and Nonclaim Markers

1. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/8, 2/8, 3/8, 2/8], [2/12, 3/12, 4/12, 3/12]], and one total parameter-independent statistic T=[0, 0, 1, 2] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

2. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/9, 2/9, 3/9, 3/9], [2/13, 3/13, 4/13, 4/13]], and one total parameter-independent statistic T=[0, 0, 2, 3] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

3. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/10, 2/10, 3/10, 4/10], [2/14, 3/14, 4/14, 5/14]], and one total parameter-independent statistic T=[0, 0, 3, 4] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

4. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/11, 2/11, 3/11, 5/11], [2/15, 3/15, 4/15, 6/15]], and one total parameter-independent statistic T=[0, 0, 4, 5] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

5. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/12, 2/12, 3/12, 6/12], [2/16, 3/16, 4/16, 7/16]], and one total parameter-independent statistic T=[0, 0, 5, 6] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

6. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/13, 2/13, 3/13, 7/13], [2/17, 3/17, 4/17, 8/17]], and one total parameter-independent statistic T=[0, 0, 6, 7] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

7. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/14, 2/14, 3/14, 8/14], [2/18, 3/18, 4/18, 9/18]], and one total parameter-independent statistic T=[0, 0, 7, 8] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

8. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/15, 2/15, 3/15, 9/15], [2/19, 3/19, 4/19, 10/19]], and one total parameter-independent statistic T=[0, 0, 8, 9] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

9. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/16, 2/16, 3/16, 10/16], [2/20, 3/20, 4/20, 11/20]], and one total parameter-independent statistic T=[0, 0, 9, 10] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

10. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/17, 2/17, 3/17, 11/17], [2/21, 3/21, 4/21, 12/21]], and one total parameter-independent statistic T=[0, 0, 10, 11] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

11. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/18, 2/18, 3/18, 12/18], [2/22, 3/22, 4/22, 13/22]], and one total parameter-independent statistic T=[0, 0, 11, 12] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

12. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/19, 2/19, 3/19, 13/19], [2/23, 3/23, 4/23, 14/23]], and one total parameter-independent statistic T=[0, 0, 12, 13] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

13. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/20, 2/20, 3/20, 14/20], [2/24, 3/24, 4/24, 15/24]], and one total parameter-independent statistic T=[0, 0, 13, 14] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

14. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/21, 2/21, 3/21, 15/21], [2/25, 3/25, 4/25, 16/25]], and one total parameter-independent statistic T=[0, 0, 14, 15] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

15. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/22, 2/22, 3/22, 16/22], [2/26, 3/26, 4/26, 17/26]], and one total parameter-independent statistic T=[0, 0, 15, 16] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

16. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/23, 2/23, 3/23, 17/23], [2/27, 3/27, 4/27, 18/27]], and one total parameter-independent statistic T=[0, 0, 16, 17] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

17. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/24, 2/24, 3/24, 18/24], [2/28, 3/28, 4/28, 19/28]], and one total parameter-independent statistic T=[0, 0, 17, 18] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

18. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/25, 2/25, 3/25, 19/25], [2/29, 3/29, 4/29, 20/29]], and one total parameter-independent statistic T=[0, 0, 18, 19] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

19. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/26, 2/26, 3/26, 20/26], [2/30, 3/30, 4/30, 21/30]], and one total parameter-independent statistic T=[0, 0, 19, 20] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

20. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/27, 2/27, 3/27, 21/27], [2/31, 3/31, 4/31, 22/31]], and one total parameter-independent statistic T=[0, 0, 20, 21] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

21. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/28, 2/28, 3/28, 22/28], [2/32, 3/32, 4/32, 23/32]], and one total parameter-independent statistic T=[0, 0, 21, 22] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

22. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/29, 2/29, 3/29, 23/29], [2/33, 3/33, 4/33, 24/33]], and one total parameter-independent statistic T=[0, 0, 22, 23] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

23. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/30, 2/30, 3/30, 24/30], [2/34, 3/34, 4/34, 25/34]], and one total parameter-independent statistic T=[0, 0, 23, 24] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

24. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/31, 2/31, 3/31, 25/31], [2/35, 3/35, 4/35, 26/35]], and one total parameter-independent statistic T=[0, 0, 24, 25] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

25. Sum Every Estimator Fibre under One Parameter Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/32, 2/32, 3/32, 26/32], [2/36, 3/36, 4/36, 27/36]], and one total parameter-independent statistic T=[0, 0, 25, 26] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0. Give (parameter, law, total_mass, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-pushforward-at-parameter · candidate

26. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/8, 2/8, 3/8, 2/8], [2/12, 3/12, 4/12, 3/12]], and one total parameter-independent statistic T=[0, 0, 1, 2] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

27. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/9, 2/9, 3/9, 3/9], [2/13, 3/13, 4/13, 4/13]], and one total parameter-independent statistic T=[0, 0, 2, 3] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

28. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/10, 2/10, 3/10, 4/10], [2/14, 3/14, 4/14, 5/14]], and one total parameter-independent statistic T=[0, 0, 3, 4] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

29. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/11, 2/11, 3/11, 5/11], [2/15, 3/15, 4/15, 6/15]], and one total parameter-independent statistic T=[0, 0, 4, 5] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

30. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/12, 2/12, 3/12, 6/12], [2/16, 3/16, 4/16, 7/16]], and one total parameter-independent statistic T=[0, 0, 5, 6] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

31. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/13, 2/13, 3/13, 7/13], [2/17, 3/17, 4/17, 8/17]], and one total parameter-independent statistic T=[0, 0, 6, 7] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

32. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/14, 2/14, 3/14, 8/14], [2/18, 3/18, 4/18, 9/18]], and one total parameter-independent statistic T=[0, 0, 7, 8] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

33. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/15, 2/15, 3/15, 9/15], [2/19, 3/19, 4/19, 10/19]], and one total parameter-independent statistic T=[0, 0, 8, 9] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

34. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/16, 2/16, 3/16, 10/16], [2/20, 3/20, 4/20, 11/20]], and one total parameter-independent statistic T=[0, 0, 9, 10] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

35. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/17, 2/17, 3/17, 11/17], [2/21, 3/21, 4/21, 12/21]], and one total parameter-independent statistic T=[0, 0, 10, 11] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

36. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/18, 2/18, 3/18, 12/18], [2/22, 3/22, 4/22, 13/22]], and one total parameter-independent statistic T=[0, 0, 11, 12] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

37. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/19, 2/19, 3/19, 13/19], [2/23, 3/23, 4/23, 14/23]], and one total parameter-independent statistic T=[0, 0, 12, 13] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

38. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/20, 2/20, 3/20, 14/20], [2/24, 3/24, 4/24, 15/24]], and one total parameter-independent statistic T=[0, 0, 13, 14] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

39. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/21, 2/21, 3/21, 15/21], [2/25, 3/25, 4/25, 16/25]], and one total parameter-independent statistic T=[0, 0, 14, 15] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

40. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/22, 2/22, 3/22, 16/22], [2/26, 3/26, 4/26, 17/26]], and one total parameter-independent statistic T=[0, 0, 15, 16] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

41. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/23, 2/23, 3/23, 17/23], [2/27, 3/27, 4/27, 18/27]], and one total parameter-independent statistic T=[0, 0, 16, 17] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

42. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/24, 2/24, 3/24, 18/24], [2/28, 3/28, 4/28, 19/28]], and one total parameter-independent statistic T=[0, 0, 17, 18] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

43. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/25, 2/25, 3/25, 19/25], [2/29, 3/29, 4/29, 20/29]], and one total parameter-independent statistic T=[0, 0, 18, 19] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

44. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/26, 2/26, 3/26, 20/26], [2/30, 3/30, 4/30, 21/30]], and one total parameter-independent statistic T=[0, 0, 19, 20] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

45. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/27, 2/27, 3/27, 21/27], [2/31, 3/31, 4/31, 22/31]], and one total parameter-independent statistic T=[0, 0, 20, 21] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

46. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/28, 2/28, 3/28, 22/28], [2/32, 3/32, 4/32, 23/32]], and one total parameter-independent statistic T=[0, 0, 21, 22] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

47. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/29, 2/29, 3/29, 23/29], [2/33, 3/33, 4/33, 24/33]], and one total parameter-independent statistic T=[0, 0, 22, 23] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

48. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/30, 2/30, 3/30, 24/30], [2/34, 3/34, 4/34, 25/34]], and one total parameter-independent statistic T=[0, 0, 23, 24] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

49. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/31, 2/31, 3/31, 25/31], [2/35, 3/35, 4/35, 26/35]], and one total parameter-independent statistic T=[0, 0, 24, 25] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

50. One Fixed Statistic Has a Family of Parameter-Indexed Laws

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/32, 2/32, 3/32, 26/32], [2/36, 3/36, 4/36, 27/36]], and one total parameter-independent statistic T=[0, 0, 25, 26] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, parameter_laws, model_qualified, certificate).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-family-readback · candidate

51. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/8, 2/8, 3/8, 2/8], [2/12, 3/12, 4/12, 3/12]], and one total parameter-independent statistic T=[0, 0, 1, 2] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=0. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

52. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/9, 2/9, 3/9, 3/9], [2/13, 3/13, 4/13, 4/13]], and one total parameter-independent statistic T=[0, 0, 2, 3] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=1. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

53. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/10, 2/10, 3/10, 4/10], [2/14, 3/14, 4/14, 5/14]], and one total parameter-independent statistic T=[0, 0, 3, 4] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=2. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

54. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/11, 2/11, 3/11, 5/11], [2/15, 3/15, 4/15, 6/15]], and one total parameter-independent statistic T=[0, 0, 4, 5] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=3. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

55. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/12, 2/12, 3/12, 6/12], [2/16, 3/16, 4/16, 7/16]], and one total parameter-independent statistic T=[0, 0, 5, 6] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=0. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

56. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/13, 2/13, 3/13, 7/13], [2/17, 3/17, 4/17, 8/17]], and one total parameter-independent statistic T=[0, 0, 6, 7] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=1. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

57. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/14, 2/14, 3/14, 8/14], [2/18, 3/18, 4/18, 9/18]], and one total parameter-independent statistic T=[0, 0, 7, 8] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=2. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

58. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/15, 2/15, 3/15, 9/15], [2/19, 3/19, 4/19, 10/19]], and one total parameter-independent statistic T=[0, 0, 8, 9] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=3. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

59. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/16, 2/16, 3/16, 10/16], [2/20, 3/20, 4/20, 11/20]], and one total parameter-independent statistic T=[0, 0, 9, 10] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=0. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

60. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/17, 2/17, 3/17, 11/17], [2/21, 3/21, 4/21, 12/21]], and one total parameter-independent statistic T=[0, 0, 10, 11] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=1. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

61. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/18, 2/18, 3/18, 12/18], [2/22, 3/22, 4/22, 13/22]], and one total parameter-independent statistic T=[0, 0, 11, 12] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=2. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

62. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/19, 2/19, 3/19, 13/19], [2/23, 3/23, 4/23, 14/23]], and one total parameter-independent statistic T=[0, 0, 12, 13] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=3. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

63. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/20, 2/20, 3/20, 14/20], [2/24, 3/24, 4/24, 15/24]], and one total parameter-independent statistic T=[0, 0, 13, 14] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=0. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

64. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/21, 2/21, 3/21, 15/21], [2/25, 3/25, 4/25, 16/25]], and one total parameter-independent statistic T=[0, 0, 14, 15] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=1. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

65. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/22, 2/22, 3/22, 16/22], [2/26, 3/26, 4/26, 17/26]], and one total parameter-independent statistic T=[0, 0, 15, 16] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=2. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

66. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/23, 2/23, 3/23, 17/23], [2/27, 3/27, 4/27, 18/27]], and one total parameter-independent statistic T=[0, 0, 16, 17] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=3. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

67. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/24, 2/24, 3/24, 18/24], [2/28, 3/28, 4/28, 19/28]], and one total parameter-independent statistic T=[0, 0, 17, 18] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=0. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

68. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/25, 2/25, 3/25, 19/25], [2/29, 3/29, 4/29, 20/29]], and one total parameter-independent statistic T=[0, 0, 18, 19] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=1. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

69. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/26, 2/26, 3/26, 20/26], [2/30, 3/30, 4/30, 21/30]], and one total parameter-independent statistic T=[0, 0, 19, 20] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=2. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

70. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/27, 2/27, 3/27, 21/27], [2/31, 3/31, 4/31, 22/31]], and one total parameter-independent statistic T=[0, 0, 20, 21] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=3. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

71. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/28, 2/28, 3/28, 22/28], [2/32, 3/32, 4/32, 23/32]], and one total parameter-independent statistic T=[0, 0, 21, 22] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=0. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

72. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/29, 2/29, 3/29, 23/29], [2/33, 3/33, 4/33, 24/33]], and one total parameter-independent statistic T=[0, 0, 22, 23] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=1. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

73. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/30, 2/30, 3/30, 24/30], [2/34, 3/34, 4/34, 25/34]], and one total parameter-independent statistic T=[0, 0, 23, 24] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=2. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

74. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/31, 2/31, 3/31, 25/31], [2/35, 3/35, 4/35, 26/35]], and one total parameter-independent statistic T=[0, 0, 24, 25] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=1; supplied outcome index=3. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

75. An Observed Estimate Is Not the Whole Sampling Law

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/32, 2/32, 3/32, 26/32], [2/36, 3/36, 4/36, 27/36]], and one total parameter-independent statistic T=[0, 0, 25, 26] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Selected parameter=0; supplied outcome index=0. Give (outcome, estimate, law, boundary).

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-observed-estimate-boundary · candidate

76. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/8, 2/8, 3/8, 2/8], [2/12, 3/12, 4/12, 3/12]], and one total parameter-independent statistic T=[0, 0, 1, 2] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

77. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/9, 2/9, 3/9, 3/9], [2/13, 3/13, 4/13, 4/13]], and one total parameter-independent statistic T=[0, 0, 2, 3] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

78. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/10, 2/10, 3/10, 4/10], [2/14, 3/14, 4/14, 5/14]], and one total parameter-independent statistic T=[0, 0, 3, 4] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

79. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/11, 2/11, 3/11, 5/11], [2/15, 3/15, 4/15, 6/15]], and one total parameter-independent statistic T=[0, 0, 4, 5] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

80. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/12, 2/12, 3/12, 6/12], [2/16, 3/16, 4/16, 7/16]], and one total parameter-independent statistic T=[0, 0, 5, 6] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

81. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/13, 2/13, 3/13, 7/13], [2/17, 3/17, 4/17, 8/17]], and one total parameter-independent statistic T=[0, 0, 6, 7] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

82. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/14, 2/14, 3/14, 8/14], [2/18, 3/18, 4/18, 9/18]], and one total parameter-independent statistic T=[0, 0, 7, 8] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

83. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/15, 2/15, 3/15, 9/15], [2/19, 3/19, 4/19, 10/19]], and one total parameter-independent statistic T=[0, 0, 8, 9] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

84. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/16, 2/16, 3/16, 10/16], [2/20, 3/20, 4/20, 11/20]], and one total parameter-independent statistic T=[0, 0, 9, 10] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

85. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/17, 2/17, 3/17, 11/17], [2/21, 3/21, 4/21, 12/21]], and one total parameter-independent statistic T=[0, 0, 10, 11] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

86. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/18, 2/18, 3/18, 12/18], [2/22, 3/22, 4/22, 13/22]], and one total parameter-independent statistic T=[0, 0, 11, 12] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

87. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/19, 2/19, 3/19, 13/19], [2/23, 3/23, 4/23, 14/23]], and one total parameter-independent statistic T=[0, 0, 12, 13] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

88. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/20, 2/20, 3/20, 14/20], [2/24, 3/24, 4/24, 15/24]], and one total parameter-independent statistic T=[0, 0, 13, 14] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

89. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/21, 2/21, 3/21, 15/21], [2/25, 3/25, 4/25, 16/25]], and one total parameter-independent statistic T=[0, 0, 14, 15] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

90. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/22, 2/22, 3/22, 16/22], [2/26, 3/26, 4/26, 17/26]], and one total parameter-independent statistic T=[0, 0, 15, 16] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

91. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/23, 2/23, 3/23, 17/23], [2/27, 3/27, 4/27, 18/27]], and one total parameter-independent statistic T=[0, 0, 16, 17] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

92. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/24, 2/24, 3/24, 18/24], [2/28, 3/28, 4/28, 19/28]], and one total parameter-independent statistic T=[0, 0, 17, 18] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

93. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/25, 2/25, 3/25, 19/25], [2/29, 3/29, 4/29, 20/29]], and one total parameter-independent statistic T=[0, 0, 18, 19] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

94. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/26, 2/26, 3/26, 20/26], [2/30, 3/30, 4/30, 21/30]], and one total parameter-independent statistic T=[0, 0, 19, 20] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

95. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/27, 2/27, 3/27, 21/27], [2/31, 3/31, 4/31, 22/31]], and one total parameter-independent statistic T=[0, 0, 20, 21] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

96. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/28, 2/28, 3/28, 22/28], [2/32, 3/32, 4/32, 23/32]], and one total parameter-independent statistic T=[0, 0, 21, 22] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

97. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/29, 2/29, 3/29, 23/29], [2/33, 3/33, 4/33, 24/33]], and one total parameter-independent statistic T=[0, 0, 22, 23] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

98. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/30, 2/30, 3/30, 24/30], [2/34, 3/34, 4/34, 25/34]], and one total parameter-independent statistic T=[0, 0, 23, 24] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

99. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/31, 2/31, 3/31, 25/31], [2/35, 3/35, 4/35, 26/35]], and one total parameter-independent statistic T=[0, 0, 24, 25] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate

100. Withholding Statistical Claims Does Not Establish Their Negations

Use common outcomes=Fin(4)={0,…,3}, parameter indices=Fin(2)={0,…,1}, complete exact law rows=[[1/32, 2/32, 3/32, 26/32], [2/36, 3/36, 4/36, 27/36]], and one total parameter-independent statistic T=[0, 0, 25, 26] in outcome order. Every law is nonnegative and sums to1. For each parameter, add law masses over each T-fibre; list the distinct statistic values increasingly, retaining zero-mass fibres. A parameter law, a sampling law and an observed estimate are different objects; no prior or posterior is supplied. Give (statistic, claim_marker_1, claim_marker_2, claim_marker_3, claim_marker_4, boundary).

The four No values withhold statistical, likelihood or posterior claims; they do not prove the corresponding properties false.

Reviewed step-by-step derivation

Sampling distribution · qf-mod36-sampling-property-nonconclusion · candidate