Statistics practice

Prior Choice and Sensitivity worksheets

Compare priors under identical data, contrast weak and strong priors, preserve zero prior mass, and justify prior choices.

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Apply identical data before comparing posteriors. Justify prior center and strength from stated background evidence.

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Keep every inferential role visible

Problems label parameters, observed data, likelihoods, priors, evidence, and posteriors before calculation. Bounded counts and printed reference values keep the work reproducible on paper.

Learning objective

What this activity builds

Assess how prior center and strength affect posterior conclusions and how additional data reduce sensitivity.

Worked example

Compare Beta(1,1) and Beta(10,10) after one success.

Beta(2,1) moves more than Beta(11,10).

The stronger prior has more prior strength and changes less.

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Make the practice count

  • Label data, hypothesis, prior, and likelihood before calculating.
  • Normalize only after forming every Bayes numerator.
  • Interpret the posterior in the parameter context.

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Math SheetNameScoreTeacherDatePrior Choice and SensitivityApply identical data to every candidate prior. Compare posterior means and strengths, note when a strong prior resists limited data, and remember that a hypothesis assigned prior probabilityzero remains at zero after ordinary updating.1.Reliable background evidence has an average success rate 0.2 and strength about 5. Choose the most defensible prior from Beta(1,4), Beta(4,1),Beta(1,4) and explain sensitivity to limited new data.Given: Prior choice should reflect both center and strength of genuine background evidence.2.Apply identical data (5 successes, 3 failures) to Beta(1,1), Beta(2,4), Beta(4,2). Compare posterior means and explain sensitivity.Given: Every prior sees the same Bernoulli data.PriorPosteriorPosterior meanBeta(1,1)Beta(6,4)0.6000Beta(2,4)Beta(7,7)0.5000Beta(4,2)Beta(9,5)0.6429Keep likelihood, prior, evidence, and posterior roles distinct.math-sheet.comPage 1 of 4
Math SheetNameScoreTeacherDatePrior Choice and SensitivityApply identical data to every candidate prior. Compare posterior means and strengths, note when a strong prior resists limited data, and remember that a hypothesis assigned prior probabilityzero remains at zero after ordinary updating.3.Compare weak Beta(1,1) and strong Beta(8,8) priors after a small batch (2 successes, 1 failures) and a larger batch (4 successes, 2 failures) with thesame success proportion 2/3.Given: Both priors see each batch, and the larger batch preserves the small batch's success-to-failure ratio.PriorBatchPosteriorMeanBeta(1,1)small 2:1Beta(3,2)0.6000Beta(1,1)large 4:2Beta(5,3)0.6250Beta(8,8)small 2:1Beta(10,9)0.5263Beta(8,8)large 4:2Beta(12,10)0.54554.A discrete prior over H1,H2,H3 is (0.00, 0.60, 0.40); likelihoods are (0.70, 0.60, 0.25). Find the posterior and explain whether strong evidence can reviveH1.Given: Ordinary updating cannot add mass where prior mass is exactly zero.Keep likelihood, prior, evidence, and posterior roles distinct.math-sheet.comPage 2 of 4
Math SheetNameScoreTeacherDatePrior Choice and SensitivityApply identical data to every candidate prior. Compare posterior means and strengths, note when a strong prior resists limited data, and remember that a hypothesis assigned prior probabilityzero remains at zero after ordinary updating.5.Compare weak Beta(1,1) and strong Beta(10,10) priors after a small batch (3 successes, 1 failures) and a larger batch (6 successes, 2 failures) with thesame success proportion 3/4.Given: Both priors see each batch, and the larger batch preserves the small batch's success-to-failure ratio.PriorBatchPosteriorMeanBeta(1,1)small 3:1Beta(4,2)0.6667Beta(1,1)large 6:2Beta(7,3)0.7000Beta(10,10)small 3:1Beta(13,11)0.5417Beta(10,10)large 6:2Beta(16,12)0.57146.A discrete prior over H1,H2,H3 is (0.00, 0.80, 0.20); likelihoods are (0.90, 0.60, 0.15). Find the posterior and explain whether strong evidence can reviveH1.Given: Ordinary updating cannot add mass where prior mass is exactly zero.Keep likelihood, prior, evidence, and posterior roles distinct.math-sheet.comPage 3 of 4
Math SheetNameScoreTeacherDatePrior Choice and SensitivityApply identical data to every candidate prior. Compare posterior means and strengths, note when a strong prior resists limited data, and remember that a hypothesis assigned prior probabilityzero remains at zero after ordinary updating.7.Reliable background evidence has an average success rate 0.2 and strength about 15. Choose the most defensible prior from Beta(3,12), Beta(4,1),Beta(1,4) and explain sensitivity to limited new data.Given: Prior choice should reflect both center and strength of genuine background evidence.8.Apply identical data (6 successes, 1 failures) to Beta(1,1), Beta(2,4), Beta(4,2). Compare posterior means and explain sensitivity.Given: Every prior sees the same Bernoulli data.PriorPosteriorPosterior meanBeta(1,1)Beta(7,2)0.7778Beta(2,4)Beta(8,5)0.6154Beta(4,2)Beta(10,3)0.7692Keep likelihood, prior, evidence, and posterior roles distinct.math-sheet.comPage 4 of 4

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