6.8.4: Case Study Analysis

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Page ID: 58314

Felix Amoruwa
De Anza College

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Case Study Analysis

The Replication Crisis: When P-Values Mislead

In 2015, the Open Science Collaboration attempted to replicate 100 published psychology studies. Only 36% of the replications produced statistically significant results (p < 0.05), compared to 97% of the original studies. This "replication crisis" shook the scientific community and raised fundamental questions about how p-values and hypothesis testing are used (and misused) in practice.

Contributing factors included: p-hacking (running many analyses and only reporting significant ones), small sample sizes producing unstable results, publication bias (journals preferring to publish significant findings), and widespread misinterpretation of what p-values actually mean.

Discussion Questions:

Explain what p-hacking is and how it can produce "significant" results from random data. If you test 20 independent hypotheses at α = 0.05, how many would you expect to be significant by chance alone?
Many researchers interpreted p < 0.05 as "strong evidence" that their hypothesis was true. What is the correct interpretation of a p-value? Why is this common misinterpretation problematic?
Some statisticians have called for abandoning p-values entirely in favor of confidence intervals and effect sizes. Argue for or against this position. What are the advantages of reporting a confidence interval alongside (or instead of) a p-value?
How does the replication crisis relate to data analytics in business? Could similar problems (multiple testing, cherry-picking results, small samples) occur when A/B testing website features or marketing campaigns? What safeguards should be in place?

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