# Interpret the key results for 1-Sample t

Complete the following steps to interpret a 1-sample t-test. Key output includes the estimate of the mean, the confidence interval, the p-value, and several graphs.

## Step 1: Determine a confidence interval for the population mean

First, consider the sample mean, and then examine the confidence interval.

The mean of the sample data is an estimate of the population mean. Because the mean is based on sample data and not on the entire population, it is unlikely that the sample mean equals the population mean. To better estimate the population mean, use the confidence interval.

The confidence interval provides a range of likely values for the population mean. For example, a 95% confidence level indicates that if you take 100 random samples from the population, you could expect approximately 95 of the samples to produce intervals that contain the population mean. The confidence interval helps you assess the practical significance of your results. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation. If the interval is too wide to be useful, consider increasing your sample size. For more information, go to Ways to get a more precise confidence interval.

## Step 2: Determine whether the test results are statistically significant

To determine whether the difference between the population mean and the hypothesized mean is statistically significant, compare the p-value to the significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.
P-value ≤ α: The difference between the means is statistically significant (Reject H0)
If the p-value is less than or equal to the significance level, the decision is to reject the null hypothesis. You can conclude that the difference between the population mean and the hypothesized mean is statistically significant. Use your specialized knowledge to determine whether the difference is practically significant. For more information, go to Statistical and practical significance.
P-value > α: The difference between the means is not statistically significant (Fail to reject H0)
If the p-value is greater than the significance level, the decision is to fail to reject the null hypothesis. You do not have enough evidence to conclude that the difference between the population mean and the hypothesized mean is statistically significant. You should make sure that your test has enough power to detect a difference that is practically significant. For more information, go to Power and Sample Size for 1-Sample t.

## Step 3: Check your data for problems

Problems with your data, such as skewness and outliers, can adversely affect your results. Use graphs to look for skewness and to identify potential outliers.