First, consider the mean difference, and then examine the confidence interval. The mean difference is the average of the differences between the paired observations in your sample.
The mean difference is an estimate of the population mean difference. Because the mean difference is based on sample data and not on the entire population, it is unlikely that the sample mean difference equals the population mean difference. To better estimate the population mean difference, use the confidence interval of the mean difference.
The confidence interval provides a range of likely values for the population mean difference. 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 difference. 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.
 
 

In these results, the estimate for the population mean difference in heart rates is 2.2. You can be 95% confident that the population mean difference is between 0.677 and 3.723.
 
 

In these results, the null hypothesis states that the mean difference in resting heart rates for patients before and after a running program is 0. Because the pvalue is 0.007, which is less than the significance level of 0.05, the decision is to reject the null hypothesis and conclude that there is a difference in the heart rates for patients before and after a running program.
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.
When data are skewed, the majority of the data are located on the high or low side of the graph. Often, skewness is easiest to detect with a histogram or boxplot.
Data that are severely skewed can affect the validity of the pvalue if your sample is small (less than 20 values). If your data are severely skewed and you have a small sample, consider increasing your sample size.
Outliers, which are data values that are far away from other data values, can strongly affect the results of your analysis. Often, outliers are easiest to identify on a boxplot.
Try to identify the cause of any outliers. Correct any data–entry errors or measurement errors. Consider removing data values for abnormal, onetime events (also called special causes). Then, repeat the analysis. For more information, go to Identifying outliers.