Statistical Significance Explained
Chris and Andres dive into the common misconception that experiments must achieve statistical significance to be valid. They discuss how to determine the appropriate level of significance based on the potential error rate you're willing to accept, emphasizing that for many experiments, especially outside of critical applications like pharmaceuticals, a higher tolerance for error can lead to faster learning and results.In this clip
From this podcast
The Startup Podcast
Edu: Product Experiments - Revealing The Science Behind Successful Apps
Related Questions
What is the importance of sample size?
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.