Statistical Transparency Debate
Loni and Kyle discuss the debate around banning p-values and confidence intervals in journals, emphasizing the importance of transparency in data analysis. Loni advocates for educating researchers on proper tool usage rather than outright bans, highlighting the need for open reporting of results and data.In this clip
From this podcast
Data Skeptic
Visualizing Uncertainty
Related Questions
Do you have any tips for analyzing data objectively and avoiding bias toward my own theory in private research involving statistical analysis?
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 are the limitations and issues with probabilistic thinking as discussed in the episode Defending the p-value and the clip Statistical Errors?