Avoiding False Positives
Chris explains how using a lower p-value cutoff and the Bonferoni correction can help reduce the chances of false positives in statistical tests. However, this approach requires a larger sample size, which may not be feasible for everyone. Kyle raises the question of conducting new analyses on different variables, but Chris emphasizes that the goal is to make the right decision most of the time, rather than avoiding false positives altogether.In this clip
From this podcast
Data Skeptic
Multiple Comparisons and Conversion Optimization
Related Questions
How is hypothesis testing used in real life as discussed in the episode False Discovery Rates and the clip 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.
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.