Hypothesis testing
Hypothesis Testing
Hypothesis testing (pronunciation: /haɪˈpɒθɪsɪs ˈtɛstɪŋ/) is a statistical method used to make inferences or draw conclusions about a population based on a sample of data. It is a fundamental procedure in statistics that allows researchers to make statistical decisions.
Etymology
The term "hypothesis testing" is derived from the Greek word "hypothesis" (meaning "assumption" or "proposition") and the English word "test" (meaning "to examine or assess").
Overview
Hypothesis testing involves the formulation of a null hypothesis (null hypothesis) and an alternative hypothesis (alternative hypothesis). The null hypothesis is a statement of no effect or no difference and is assumed to be true until evidence suggests otherwise. The alternative hypothesis is a statement that directly contradicts the null hypothesis.
The process of hypothesis testing involves four steps:
- Formulate the null and alternative hypotheses
- Choose the significance level (significance level)
- Calculate the test statistic (test statistic)
- Make a decision based on the test statistic and the significance level
Related Terms
- Null Hypothesis: A statement that there is no effect or difference in the population; the statement being tested in a hypothesis test.
- Alternative Hypothesis: A statement that contradicts the null hypothesis; the statement that the researcher hopes to support.
- Significance Level: The probability of rejecting the null hypothesis when it is true.
- Test Statistic: A quantity calculated from the sample data that is used in making a decision about the rejection of the null hypothesis.
- P-value: The probability of obtaining a result as extreme as, or more extreme than, the result actually obtained, assuming that the null hypothesis is true.
- Type I Error: The error of rejecting a true null hypothesis.
- Type II Error: The error of failing to reject a false null hypothesis.
See Also
This WikiMD.org article is a stub. You can help make it a full article.