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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- Alternative hypothesis
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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
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Rejecting the null hypothesis when it is actually true; a false positive.- Type II error
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Failing to reject the null hypothesis when it is actually false; a false negative.- Significance level
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The threshold probability for rejecting the null hypothesis; denoted alpha. Commonly set at 0.05 or 0.01.- Statistical significance
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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
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The probability of making a Type II error.- Power
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The probability of correctly rejecting the null hypothesis when it is actually false; equals 1 minus beta.- One-tailed test
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A hypothesis test where the alternative hypothesis specifies the direction of the difference or effect (greater than or less than).- Two-tailed test
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A hypothesis test where the alternative hypothesis specifies only that a difference or effect exists, without specifying direction.- Critical value
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The boundary value of the test statistic that marks the edge of the rejection region.- Rejection region
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The range of test statistic values that lead to rejecting the null hypothesis.- Test statistic
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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
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A measure of the magnitude or practical importance of the difference or relationship being tested, independent of sample size.- Confidence level
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The probability, expressed as a percentage, that a confidence interval contains the true population parameter; equals 1 minus the significance level.- Degrees of freedom
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The number of values in a calculation that are free to vary; used to determine critical values in many tests.
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