0:05 Presentation Introduction
0:37 Agenda
0:59 Speaker Introduction
1:10 P-Values are a way of quantifying how extreme your data are
3:44 Why can’t p-values be used to accept the null hypothesis?
4:28 Comparing the extremes of probability distributions with simulated coin flip data
6:50 A flat p-value distribution indicates that the p-value cannot be used to accept the null hypothesis (in this context)
7:21 P-values: pros and cons
8:52 Frequentist vs. Bayesian statistics
9:45 Additional PyData presentation on hypothesis testing
10:29 Talk Summary
URL: https://www.youtube.com/watch?v=QVfgqkTv7b8
Contents
0:05 Presentation Introduction 0:37 Agenda 0:59 Speaker Introduction 1:10 P-Values are a way of quantifying how extreme your data are 3:44 Why can’t p-values be used to accept the null hypothesis? 4:28 Comparing the extremes of probability distributions with simulated coin flip data 6:50 A flat p-value distribution indicates that the p-value cannot be used to accept the null hypothesis (in this context) 7:21 P-values: pros and cons 8:52 Frequentist vs. Bayesian statistics 9:45 Additional PyData presentation on hypothesis testing 10:29 Talk Summary