Summary
P-values give the probability of data under the null; corrections like Bonferroni control false positives across many tests.
In depth
A p-value is not the probability the null is true. Testing many hypotheses inflates false positives: Bonferroni divides alpha by number of tests; Benjamini-Hochberg controls false discovery rate. p-hacking and publication bias are problems.
Pre-registration limits flexibility in analysis. False discovery rate is common in omics. Effect size and confidence interval are more informative than p alone.
Key idea: if you test 20 hypotheses, one is expected to be 'significant' by chance.
Frequently asked questions
What is P-Values and Multiple Testing?
P-values give the probability of data under the null; corrections like Bonferroni control false positives across many tests.
Which area of biology covers P-Values and Multiple Testing, and how advanced is it?
P-Values and Multiple Testing is one of the 51 topics in Lab Methods & Biostatistics. It is rated Advanced, so it suits learners who want specialist depth. Related topics in this area include Statistical Power, Experimental Design, Meta-Analysis.
Can you give an example of P-Values and Multiple Testing?
Genome-wide studies use p < 5×10^-8.
What should I learn before P-Values and Multiple Testing?
Work through the Beginner topics in Lab Methods & Biostatistics first, such as Scientific Method, Hypothesis, Variables, then return to P-Values and Multiple Testing. Advanced topics assume the Intermediate vocabulary.
What topics are related to P-Values and Multiple Testing?
The closest topics to P-Values and Multiple Testing are Statistical Power, Experimental Design, Meta-Analysis, Null Hypothesis. Comparing them side by side is a good way to see how the ideas connect.
How can I check that I understand P-Values and Multiple Testing?
Read the summary and the in-depth section, then use the quick check on the interactive lesson for P-Values and Multiple Testing. To practise the whole of Lab Methods & Biostatistics, use the Lab Methods & Biostatistics quiz.
How is P-Values and Multiple Testing different from Statistical Power?
P-Values and Multiple Testing: P-values give the probability of data under the null; corrections like Bonferroni control false positives across many tests. Statistical Power: The probability of detecting a true effect, determined by sample size, effect size and variability. Read both side by side and note what each one explains.
How should I study P-Values and Multiple Testing step by step?
First, read the summary and write the definition in your own words. Second, read the in-depth section and note any example. Third, compare it with Statistical Power and explain the difference aloud. Finally, take the Lab Methods & Biostatistics quiz and come back to this page for any question you miss.