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#269 - Good vs. bad science: how to read and understand scientific studies

The Peter Attia Drive

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Understanding P Values and False Positive Rate in Clinical Trials

The p-value is a common term, but many people don't realize it represents the false positive rate. The false negative rate, known as beta, is less well-known. A p-value of 0.05 or less is considered significant. It represents the probability of a false positive. An example is a study where coffee appears to darken eye color, with a p-value of 0.17 suggesting a 17% chance of a false positive. Statistical significance means rejecting the null hypothesis if the p-value is less than a predefined level. The default assumption is that there is no difference between groups. P-values are often seen at 0.05, an arbitrary threshold. A lower p-value risks discarding relevant information, while a higher p-value risks false negatives. The false negative rate is typically 10-20%, with power being one minus the false negative rate. Understanding these concepts is crucial for designing clinical trials.

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