P-Value

A p-value is a statistical measure used in hypothesis testing to quantify the evidence against a null hypothesis. It represents the probability of obtaining results at least as extreme as the observed data, assuming the null hypothesis is true. Lower p-values indicate stronger evidence against the null hypothesis, often used to determine statistical significance in research.

Also known as: p value, probability value, p, p-val, significance probability

Why learn P-Value?

Developers should learn about p-values when working with data analysis, machine learning, or A/B testing to make informed decisions based on statistical evidence. It is crucial for validating models, interpreting experimental results, and ensuring data-driven conclusions in fields like data science, bioinformatics, and quantitative research.

Learning Resources

• video: Khan Academy: P-values and significance tests
• video: StatQuest: P-values Clearly Explained
• docs: Wikipedia: P-value
• course: Coursera: Statistics with R Specialization
• docs: NIST/SEMATECH e-Handbook of Statistical Methods