Null Hypothesis Significance Testing (NHST)
Definition
NHST evaluates observations under a specified null hypothesis.
A testing procedure that evaluates data under a specified null hypothesis and often compares a p-value with a predetermined threshold.
Simply explained
One checks how well the measured data fit a previously specified baseline assumption.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
An A/B test checks whether the observed difference is compatible with a null hypothesis.
Limits and pitfalls
- Interpret p-value as effect size, error probability of the hypothesis, or proof of practical relevance.
Origin & history
NHST originated from the work of Fisher as well as Neyman and Pearson; its interpretation is still debated today.
Use in mid-sized companies
- Possible application: An A/B test checks whether the observed difference is compatible with a null hypothesis.
Frequently asked questions
What does null hypothesis significance testing mean?
A testing procedure that evaluates data under a specified null hypothesis and often compares a p-value with a predetermined threshold.
Which mistakes should be avoided?
Interpret p-value as effect size, error probability of the hypothesis, or proof of practical relevance.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: