Statistical Significance and Plausible Effect Sizes
John Ioannidis discusses different approaches to approximating the true effect size in studies and the power to detect these plausible effect sizes. He explores the concept of statistical significance at the 0.5 level and the importance of sample size in obtaining statistically significant results.In this clip
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EconTalk
John Ioannidis on Statistical Significance, Economics, and Replication
Related Questions
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.
What is the biggest effect size?
What is the importance of sample size?