# Select the analysis options for Sample Size for Estimation

Stat > Power and Sample Size > Sample Size for Estimation > Options

## Confidence level

In Confidence level, enter the level of confidence for the confidence interval. Usually, a confidence level of 95% works well. A 95% confidence level indicates that, if you take 100 random samples from the population, the confidence intervals for approximately 95 of the samples will contain the population parameter.

For a given set of data, a lower confidence level produces a narrower confidence interval, and a higher confidence level produces a wider confidence interval. The width of the interval also tends to decrease with larger sample sizes. Therefore, you may want to use a confidence level other than 95%, depending on your sample size.
• If your sample size is small, a 95% confidence interval may be too wide to be useful. Using a lower confidence level, such as 90%, produces a narrower interval. However, the likelihood that the interval contains the population parameter decreases.
• If your sample size is large, consider using a higher confidence level, such as 99%. With a large sample, a 99% confidence level may still produce a reasonably narrow interval, while also increasing the likelihood that the interval contains the population parameter.

## Confidence interval

Confidence interval
• Lower bound: Use this one-sided test to determine whether the population parameter is less than the hypothesized value. This one-sided test has greater power than a two-sided test, but it cannot detect whether the population parameter is greater than the hypothesized value.

• Two-sided: Use this two-sided test to determine whether the population parameter differs from the hypothesized value. This two-sided test can detect differences that are less than or greater than the hypothesized value, but it has less power than a one-sided test.

• Upper bound: Use this one-sided test to determine whether the population parameter is greater than the hypothesized value. This one-sided test has greater power than a two-sided test, but it cannot detect whether the population parameter is less than the hypothesized value.

For more information on selecting a one-sided or two-sided alternative hypothesis, go to About the null and alternative hypotheses.

## Assume population standard deviation is known

Select this option when you know the value of the population standard deviation and you want to estimate the sample size or margin of error for 1-Sample Z. If you do not know the standard deviation of the population, use 1-Sample t instead. This option is only available when the parameter of interest is the mean.

## "Length" of observation (time, items, area, volume, etc.)

If you want to perform a 1-sample Poisson rate test, you can enter a value to divide the sample rate of occurrence into a more useful form (sample rate of occurrence÷length of observation). For example, a manufacturer records defects quarterly, but needs to convert them into a monthly defect rate for its reports. An analyst enters 3 to divide the quarterly rate by 3 to find the monthly defect rate.

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