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.
 
 

In these results, the estimate of the population mean for energy cost is 330.56. You can be 95% confident that the population mean is between 266.92 and 394.20.
 
 

In these results, the null hypothesis states that the mean energy cost equals 200. Because the pvalue is 0.0003, which is less than the significance level of 0.05, the decision is to reject the null hypothesis and conclude that the population mean cost is different from 200.
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.