Interpreting mean and standard deviation excel bar graphs

#Interpreting mean and standard deviation excel bar graphs how to

The 95% confidence interval (CI) of the mean difference was from 0.23 mmol/L (for the lower bound value to 2 decimal places) to 0.79 mmol/L (for the upper bound value). Therefore, there was a mean difference of 0.51 mmol/L between the control group and exercise group, with cholesterol concentration being 0.51 mmol/L higher in the control group (i.e., 5.064 вЂ“ 4.554 = 0.51 mmol/L). There were 11 participants in the exercise group. In the ' exercise group', mean cholesterol concentration was 4.55 mmol/L with a standard deviation of 0.33 mmol/L. There were 10 participants in the control group. The results show that the mean cholesterol concentration in the ' control group' was 5.06 mmol/L (to 2 decimal places) with a standard deviation of 0.28 mmol/L. The highlighted t.test output at the bottom above presents the 95% confidence interval (CI) of the mean difference. The mean_difference output on the right above presents the mean difference (i.e., under the column). The descriptives output on the left above presents the mean (i.e., under the column), standard deviation (i.e., under the column) and sample size (i.e., under the column) for the ' control group' and ' exercise group' (i.e., along rows and respectively). To start your analysis, look at the useful descriptive statistics that were produced when you ran: (a) the descriptives (b) the mean_difference and (c) t.test procedures earlier, as shown side-by-side below:

#Interpreting mean and standard deviation excel bar graphs how to

However, in this 'quick start' guide, we simply show you how to interpret the results from the independent-samples t-test, assuming your data has already met these important assumptions. Therefore, it is extremely important that you first check whether your data meets these three assumptions before interpreting the results from the independent-samples t-test.

For example, if our data did not meet any of these three assumptions, the results for the independent-samples t-test discussed in this section might be invalid/inaccurate and it is possible that the conclusions would also be incorrect. In this section, we explain how to interpret these results, assuming your data has already met (i.e., 'passed') the assumptions of no significant outliers ( Assumption #4), a dependent variable that is approximately normally distributed for each category of your independent variable ( Assumption #5), and homogeneity of variances ( Assumption #6), as discussed in the Assumptions section earlier. R and RStudio Interpreting the independent-samples t-test resultsДЄfter carrying out the t.test, descriptives and mean_difference procedures using R in the previous two sections, RStudio will display a set of results that contain all the information you need to interpret and report the results from an independent-samples t-test. Therefore, start by learning how to interpret the independent-samples t-test results in the next section. Finally, we explain how you can report your results in-text and using a graph. On this page you will learn how to interpret the results for the independent-samples t-test, as well as descriptive statistics that include the group means, standard deviations, sample sizes, and the mean difference. On the previous page you learnt how to carry out an independent-samples t-test, including useful descriptive statistics.

Independent-samples t-test using R, Excel and RStudio (page 4)