About this module
An estimate changes when the sample changes.
This module turns that fact into sampling distributions, standard errors, confidence intervals, and tests.
We will separate uncertainty in an average from variation in individual customers, and avoid turning significance thresholds into certainty or business-value scores.
Lessons
- Lesson 71
Parameters, Statistics, Estimands, and Estimators
Choosing the population question before calculating an answer
2:32 - Lesson 72
Sampling Distributions
Separating variation in customers from variation in an estimate
2:29 - Lesson 73
Estimator Bias, Consistency, Efficiency, and Robustness
Comparing estimators on error rather than a fashionable label
2:44 - Lesson 74
Law of Large Numbers
Why more observations can stabilize a rate without a monotone path
2:44 - Lesson 75
Central Limit Theorem
Why averages may look normal when individual amounts do not
2:40 - Lesson 76
Standard Error
Measuring uncertainty in a mean rather than variation among payments
2:38 - Lesson 77
Confidence Intervals and Coverage
Interpreting interval uncertainty as repeated coverage
2:39 - Lesson 78
Null and Alternative Hypotheses
Testing an explicit baseline rather than a vague improvement claim
2:36 - Lesson 79
P-Values, Significance, Type I/II Errors, and Power
Preventing p-values from becoming certainty scores
2:36 - Lesson 80
Effect Size, Practical Significance, and Multiple Comparisons
Separating a detectable effect from a worthwhile change
2:56