# Enter your data for Power and Sample Size for 1-Sample t

Stat > Power and Sample Size > 1-Sample t

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.
###### Tip

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.

• Differences: Enter one or more values to specify the difference that you want to detect between a mean and a hypothesized mean (target value). Usually, you enter the smallest difference that has practical consequences for your application.
###### Note

If you select Less than on the Options sub-dialog box, enter a negative difference. Otherwise, enter a positive difference.

• Power values: Enter one or more values to specify the probability that the test detects the difference between the means when a difference actually exists. Common values are 0.8 and 0.9. For example, analysts enter 0.9 because they want a 90% chance that the test will detect an important difference between the mean width of dowels and the target width when a difference actually exists.

2. In Standard deviation, enter an estimate of the standard deviation of the population (denoted as σ or sigma). If you have not collected the data, use an estimate of the standard deviation for the population. Base your estimate on related research, design specifications, pilot studies, subject-matter knowledge, or similar information. If you have already collected and analyzed the data, use the standard deviation of the sample.

If you know the standard deviation of the population, use Power and Sample Size for 1-Sample Z because the Z-test has more power than the t-test.

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