Enter your data for Power and Sample Size for 1 Proportion

Stat > Power and Sample Size > 1 Proportion

Complete the following steps to specify the data for the power and sample size calculation.

  1. Specify values for two of the following power function variables. Leave the variable that you want to calculate blank.
    • Sample sizes: Enter a sample size of interest. To assess the effect of different sample sizes, enter multiple values. Larger sample sizes give the test more power to detect a difference.

      If you enter multiple values into a field, separate the values with a space. You can also use shorthand notation to indicate multiple values. For example, you can enter 10:40/5 to indicate sample sizes from 10 to 40 in increments of 5.

    • Comparison proportions: Enter one or more proportions that are meaningfully different from the Hypothesized proportion. Usually, the difference between the comparison proportion and hypothesized proportion is the smallest difference that has practical consequences for your application.

      For example, a marketing analyst wants to determine whether the proportion of customers who respond to a survey differs from the national average of 4.3%. Specifically, the analyst wants to know whether the proportion differs by 1% from the national average, and enters 0.033 and 0.053.

    • Power values: Enter one or more values to specify the probability that the test detects a difference between each comparison proportion and the hypothesized proportion when a difference actually exists. Common values are 0.8 and 0.9. For example, a marketing analyst enters 0.9 to have a 90% chance that the test will detect an important difference between the proportion of customers who respond to the survey and the national average (hypothesized proportion) when a difference actually exists.
  2. Enter a value in Hypothesized proportion. The Hypothesized proportion defines your null hypothesis (H0: ρ = ρ0). Think of this value as a target or a reference value. For example, an analyst enters 0.043 to determine whether the proportion of customers that respond to a direct-mail offer is different from 4.3% (H0: ρ = 0.043).
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