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Publication date: 07/24/2024

Power for Two Independent Sample Proportions

Use the Power for Two Independent Sample Proportions Explorer to determine a sample size for a hypothesis test for proportions from two groups. Select DOE > Sample Size Explorers > Power > Power for Two Independent Sample Proportions. Explore the trade offs between sample size, power, significance, and the hypothesized difference to detect. Sample size and power are associated with the following hypothesis test:

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versus the two-sided alternative:

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or versus either of the following one-sided alternatives:

Equation shown here or Equation shown here

where p1 and p2 are the population proportions from two populations, and D0 is the hypothesized difference in proportions.

Power Explorer for Two Independent Sample Proportions Settings

Set study assumptions and explore sample sizes using the radio buttons, text boxes, and menus. The profiler updates as you make changes to the settings. Alternatively, change settings by dragging the cross hairs on the profiler curves.

Test Type

Specifies a one or two-sided hypothesis test.

Preliminary Information

Alpha

Specifies the probability of a type I error, which is the probability of rejecting the null hypothesis when it is true. It is commonly referred to as the significance level of the test. The default alpha level is 0.05.

Power Explorer for Two Independent Sample Proportions Profiler

The profiler enables you to visualize the impact of sample size assumptions on the power calculations.

Total Sample Size

Specifies the total number of observations (runs, experimental units, or samples) needed for your experiment. Select Lock to lock the total sample size.

Solve for

Enables you to solve for a sample size or a group proportion.

Power

Specifies the probability of rejecting the null hypothesis when it is false. With all other parameters fixed, power increases as sample size increases.

Group 1 Sample Size

Specifies the number of observations (runs, experimental units, or samples) needed for Group 1 in your experiment.

Group 2 Sample Size

Specifies the number of observations (runs, experimental units, or samples) needed for Group 2 in your experiment.

Group 1 Proportion

Specifies the proportion that you assume for Group 1.

Group 2 Proportion

Specifies the proportion that you assume for Group 2.

Note: Adjusting the sample size for one group adjusts the total sample size unless the total sample size is locked. In that case, adjusting the sample size for one group adjust the sample size for the second group. Use the text boxes to specify group sample sizes.

Power Explorer for Two Independent Sample Proportions Options

The Explorer red triangle menu and report buttons provide additional options:

Simulate Data

Opens a data table of simulated data based on the explorer settings. View the simulated response column formula for the settings used.

Make Data Collection Table

Creates a new data table that you can use for data collection. The table includes scripts to facilitate data analysis.

Save Settings

Saves the current settings to the Saved Settings table. This enables you to save a set of alternative study plans. See Saved Settings in the Sample Size Explorers.

Reset to Defaults

Resets all parameters and graphs to their default settings.

Help

Opens JMP online help.

Statistical Details for the Power Explorer for Two Independent Sample Proportions

The power calculations for testing the difference in proportions from two sample groups are based on the normal approximation. The calculations depend on the form of the alternative hypothesis. For a one-sided, higher alternative (p1 > p2):

Equation shown here

For a one-sided, lower alternative (p1 < p2):

Equation shown here

For a two-sided alternative (p1p2):

Equation shown here

Equation shown here

where:

α is the significance level

n1 and n2 are the group sample sizes

p1 and p2 are the group proportions

δ is the difference to detect

z1-α is the (1 - α)th quantile of the distribution

Φ(x) is the cumulative distribution function of the normal distribution.

Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).