Null and alternative hypotheses, p-values, significance levels, and Type I and II errors. Front: the term. Back: a plain-language definition.
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- Null hypothesis
- The default claim stating there is no effect, no difference, or no relationship; denoted H0.
- Alternative hypothesis
- The claim being tested as a replacement for the null; states that an effect, difference, or relationship exists; denoted H1 or Ha.
- Type I error
- Rejecting the null hypothesis when it is actually true; a false positive.
- Type II error
- Failing to reject the null hypothesis when it is actually false; a false negative.
- P-value
- The probability of observing a test result as extreme as the one obtained, assuming the null hypothesis is true.
- Significance level
- The threshold probability for rejecting the null hypothesis; denoted alpha. Commonly set at 0.05 or 0.01.
- Statistical significance
- The result of a test where the p-value is less than the chosen significance level; indicates the result is unlikely under the null hypothesis.
- Beta
- The probability of making a Type II error.
- Power
- The probability of correctly rejecting the null hypothesis when it is actually false; equals 1 minus beta.
- One-tailed test
- A hypothesis test where the alternative hypothesis specifies the direction of the difference or effect (greater than or less than).
- Two-tailed test
- A hypothesis test where the alternative hypothesis specifies only that a difference or effect exists, without specifying direction.
- Critical value
- The boundary value of the test statistic that marks the edge of the rejection region.
- Rejection region
- The range of test statistic values that lead to rejecting the null hypothesis.
- Test statistic
- A value calculated from the sample data using a specific formula; compared to the critical value to decide whether to reject the null.
- Effect size
- A measure of the magnitude or practical importance of the difference or relationship being tested, independent of sample size.