Statistics Dictionary

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One-Sample z-Test

A one-sample z-test is used to test whether a population parameter is significantly different from some hypothesized value.

Here is how to use the test.

  • Define hypotheses. The table below shows three sets of null and alternative hypotheses. Each makes a statement about how the true population mean μ is related to some hypothesized value M. (In the table, the symbol ≠ means " not equal to ".)

    Set Null hypothesis Alternative hypothesis Number of tails
    1 μ = M μ ≠ M 2
    2 μ > M μ < M 1
    3 μ < M μ > M 1
  • Specify significance level. Often, researchers choose significance levels equal to 0.01, 0.05, or 0.10; but any value between 0 and 1 can be used.

  • Compute test statistic. The test statistic is a z-score (z) defined by the following equation.

    z = (x - M ) / [ σ /sqrt(n) ]

    where x is the observed sample mean, M is the hypothesized population mean (from the null hypothesis), and σ is the standard deviation of the population.

  • Compute P-value. The P-value is the probability of observing a sample statistic as extreme as the test statistic. Since the test statistic is a z-score, use the Normal Distribution Calculator to assess the probability associated with the z-score.

  • Evaluate null hypothesis. The evaluation involves comparing the P-value to the significance level, and rejecting the null hypothesis when the P-value is less than the significance level.

The one-sample z-test can be used when the population is normally distributed, and the population variance is known.

See also:   Hypothesis Test for Proportions