仮説
Hypothesis / ハイポセシス
A hypothesis is a testable statement that can be evaluated with data, often framed as null and alternative hypotheses.
A hypothesis defines a specific claim about a population or relationship that can be tested through evidence. In statistical testing, the null hypothesis represents no effect, while the alternative represents a meaningful difference or relationship. Clear hypotheses guide experiment design, sample size decisions, and interpretation of results.
It determines the experiment design and what data are required. It shapes which metrics and thresholds indicate success or failure. It influences how confidently results can be acted upon.
- It determines the experiment design and what data are required.
- It shapes which metrics and thresholds indicate success or failure.
- It influences how confidently results can be acted upon.
- State hypotheses in measurable terms with defined variables.
- Specify null and alternative hypotheses before testing.
- Choose sample sizes that can detect meaningful effects.
- Interpret results in context, not just by p-values.
- Document assumptions so others can replicate the test.
A product team tests whether a new onboarding flow increases activation. The null hypothesis states there is no difference, and the alternative states activation increases by at least 5%. They run an A/B test with a sample size large enough to detect the effect. Results show a statistically significant increase, and the team rolls out the change while documenting assumptions and limitations.
Compare Hypothesis with adjacent concepts before deciding. Hypothesis | Current concept | Use when the team needs the primary decision lens Adjacent metric or framework | Supporting lens | Use when the team needs evidence or process detail General vocabulary | Broad explanation | Use only for orientation, not final decision-making
| Metric | Difference | Why read together |
|---|---|---|
| Hypothesis | Current concept | Use when the team needs the primary decision lens |
| Adjacent metric or framework | Supporting lens | Use when the team needs evidence or process detail |
| General vocabulary | Broad explanation | Use only for orientation, not final decision-making |
- A hypothesis is not a casual guess; it is a testable statement.
- Failing to reject the null does not prove the null is true.
- Changing hypotheses after seeing data undermines validity.
When should I use Hypothesis?
Use it when the team needs to decide scope, priority, owner, or trade-off, not when it only needs a short definition.
What makes Hypothesis useful in practice?
It becomes useful when it is tied to evidence, a decision owner, and a concrete next operating choice.
What should I avoid?
Avoid using the term as a label without clarifying assumptions, boundaries, and how success will be judged.