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A common statistical misconception is the belief that $$1 - p$$ equals the probability that a statistically significant result will be successfully replicated. Researchers often over-optimistically assume that low $$p$$-values guarantee replication, while actual statistical power requirements for high replication probability are typically much more demanding.

Misinterpreting p-values as the Probability of Replication

In a study by Oakes (1986), $$60\%$$ of professional researchers incorrectly believed that a $$p$$-value of $$0.01$$ in an independent-samples $$t$$-test with $$20$$ participants in each sample indicated a $$99\%$$ probability that the results would replicate. This finding demonstrates a widespread misunderstanding of statistical significance even among experienced psychological researchers.

Example of Researcher Misinterpretation of p-values

If a researcher obtains a statistically significant result, calculating 1 minus the p-value (1 - p) will give them the exact probability that their result will be successfully replicated.

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