🎯 Do you really understand p-values?
The p-value histogram can reveal a LOT about your data. Let's break it down using real examples.👇
The p-value histogram can reveal a LOT about your data. Let's break it down using real examples.👇
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What does that mean? 🤔
If there’s truly no difference between groups, the p-value behaves like rolling a fair die:
• P(p < 0.01) = 0.01
• P(p < 0.02) = 0.02
This randomness underlies why p-values are so tricky—and why multiple testing correction is critical
When you analyze RNA-seq, ChIP-seq, or other large datasets, you’re running thousands of tests.
Plotting the p-value histogram can tell you:
✔️ If the null hypothesis holds
✔️ If your experiment reveals meaningful signals
📊 [0.01] [0.02] [0.03] ... evenly distributed across the range 0 to 1.
If you see a lot of p-values near zero, it suggests true effects in your data—signals worth exploring!
This often indicates technical artifacts in your data (batch effects, low-quality samples).
Always check your p-value histogram before jumping to conclusions. reference: https://varianceexplained.org/statistics/interpreting-pvalue-histogram/