Statistical Fallacies
Andrew explains the fallacy of statistically significant results and how researcher degrees of freedom can lead to biased estimates. He highlights the flaws in the claim that there is a less than 5% chance of extreme results occurring if nothing is going on.In this clip
From this podcast
EconTalk
Andrew Gelman on Social Science, Small Samples, and the Garden of the Forking Paths
Related Questions
How is hypothesis testing used in real life as discussed in the episode Andrew Gelman on Social Science, Small Samples, and the Garden of the Forking Paths and the clip Statistical Significance Explained?
I have a question about this episode John Ioannidis on Statistical Significance, Economics, and Replication and this Rethinking Statistical Significance. A researcher is studying mirex contamination in farmed salmon. He first found a 95% confidence interval for the mean concentration to be 0.0834 to 0.0992 parts per million. Later, he rejected the null hypothesis that the mean did not exceed the EPA's recommended safe level of 0.08 ppm based on a P-value of 0.0027. Explain how these two results are consistent, discussing the confidence level, the P-value, and the decision.
How is hypothesis testing used in real life as discussed in the episode False Discovery Rates and the clip Statistical Significance from the episode "Andrew Gelman on Social Science, Small Samples, and the Garden of the Forking Paths"?