Quick answer: Hypothesis testing in biology is a five-step loop: state H₀ (no effect) and H₁, set α (usually 0.05), pick a test matched to your data and design, compute the statistic and p-value, then decide — reject H₀ if p < α. The whole machinery exists because biology is noisy: you can never "prove" an effect, only bound how surprising your data would be if there were none. The general logic is in our hypothesis testing guide; here it’s applied to wet-lab and field data.
Table of Contents
The five steps on a real bio example
Question: does a new fertilizer change plant height after 8 weeks? 20 plants per treatment.
- H₀: mean height fertilized = mean height control (the fertilizer does nothing). H₁: the means differ.
- α = 0.05 — the false-positive rate you accept up front, never after seeing the data.
- Test: two independent groups, one continuous outcome → independent-samples t-test (check roughly normal residuals; with n=20 per group it’s forgiving).
- Compute: t = difference in means ÷ standard error of that difference; software gives p directly.
- Decide: p = 0.003 < 0.05 → reject H₀; the data are unlikely under "no effect." Report the means, not just "significant."
Matching the test to the biological design
| Design | Outcome | Test |
|---|---|---|
| Two independent groups (treated vs control) | Continuous | Independent t-test (Mann-Whitney if very non-normal) |
| Same subjects before/after, paired organs | Continuous | Paired t-test |
| Three+ groups (doses, species, sites) | Continuous | One-way ANOVA + post-hoc (Tukey) |
| Counts in categories (infected/not by sex) | Counts | Chi-square / Fisher’s exact (small n) |
| Association between two measurements | Continuous | Correlation / regression |
The three mistakes biology reviews catch
- Pseudoreplication: treating 50 cells from one culture flask as n=50. The replicate is the flask, not the cell — inflate n and every p-value becomes meaningless.
- p-hacking by iteration: adding animals or repeating the assay until p dips under 0.05. Decide n and stopping rules before starting; a p-value near 0.06 is “not demonstrated,” not “almost there.”
- Confusing significance with size: a huge n makes a 0.4% difference “significant.” Always report effect size with a confidence interval next to the p — the meaning of p is covered step by step in our p-value explainer.
Context for the wider toolkit — data types, distributions, sampling — is in our introduction to biostatistics, and why biology leans on statistics at all in this overview. To run these tests yourself, point-and-click, our jamovi guide walks t-tests and ANOVA with zero code.
For one-to-one coaching on designing your thesis experiments and analyses, Ampersand Academy teaches biostatistics and R one-to-one.
Official documentation
For the full reference behind this guide, read the NCBI Bookshelf entry on statistical hypothesis testing. It is the source the details above are drawn from, and the first place to check when an option or attribute name changes.
Frequently asked questions
What does p < 0.05 actually mean in a biology experiment?
If there were truly no effect, results at least this extreme would occur less than five percent of the time. It is a statement about the data under the null hypothesis – not the probability the hypothesis is true.
Why can a study never prove the alternative hypothesis?
Hypothesis tests only measure how incompatible the data are with the null. Evidence accumulates across replication and design quality; a single significant result supports, but cannot prove, a biological claim.
What is pseudoreplication and why is it fatal?
It is counting subsamples from one biological unit as independent replicates – 50 cells from one flask as n=50. The true sample size is the flask, so the test dramatically overstates certainty.
When should I use ANOVA instead of multiple t-tests?
Whenever you compare three or more groups. Running all pairwise t-tests inflates the false-positive rate; ANOVA gives one overall test, then post-hoc comparisons like Tukey’s control for multiplicity.
Is p = 0.06 a failed experiment?
No. It means the effect was not demonstrated at your chosen threshold with this sample size. Report the effect size and confidence interval – the result may be biologically meaningful and detectable with more power.
Last updated on · Written by Dinesh Kumar R
