The t-test is a statistical procedure used to determine whether there is a significant difference between the means of two groups. When writing the results of a t-test in APA style, it is essential to report the relevant statistical information clearly and concisely. Here is a step-by-step guide on how to do so:
1. Start by stating the purpose of the t-test. This should include a brief description of the research question or hypothesis being tested. 2. Report the sample size for each group. It is essential to specify whether the samples are independent or paired. 3. Report the mean and standard deviation for each group. These values provide information about the data distribution and allow readers to gauge the spread of the scores. 4. Report the t-statistic and associated degrees of freedom (df). The t-statistic is a measure of the difference between the means of the two groups, and the degrees of freedom represent the number of scores that are free to vary within the sample. 5. Report the p-value. The p-value measures the probability that the observed difference between the means could have occurred by chance. A p-value of less than 0.05 is generally considered statistically significant, meaning that the observed difference is unlikely to have occurred by chance. 6. Report the effect size. In addition to the statistical significance, it’s crucial to report the effect size, which measures the magnitude of the difference between the means of the two groups. Effect size provides context for the practical significance of the results. The commonly used effect size measure for t-tests is Cohen’s d. Make sure to include the effect size value in your results section and an interpretation based on established guidelines (e.g., small, medium, or large effect). 7. Interpret the results. Based on the t-statistic, degrees of freedom, and p-value, you can determine whether the observed difference between the means of the two groups is statistically significant. If the p-value is less than 0.05, you can conclude that there is a statistically significant difference between the means of the two groups. If the p-value is greater than 0.05, you can conclude that there is no statistically significant difference between the means of the two groups. 8. Report any additional relevant information. If you conducted other relevant findings or statistical tests, be sure to include this information in your results section.
“ The purpose of this study was to examine the effect of a new study strategy on test performance. A total of 50 students were randomly assigned to either the experimental group (n = 25) or the control group (n = 25). The experimental group received training on the new study strategy, while the control group received no intervention. The mean test score for the experimental group was 85, with a standard deviation of 10. The mean test score for the control group was 80, with a standard deviation of 15. A paired-samples t-test was conducted to compare the means of the two groups. The t-statistic was 2.17, with df=49 (p < .05).The effect size for the difference between the groups was calculated using Cohen’s d, resulting in a value of 0.39, which is considered a small to medium effect. The results of this study indicate that there is a statistically significant difference between the mean test scores of the experimental group and the control group. Specifically, the experimental group had a higher mean test score than the control group. These findings suggest that the new study strategy effectively improved test performance, with a small to medium effect size.“
In addition to reporting the statistical significance of the results of a t-test, it is also essential to report the effect size. The effect size measures the strength of the relationship between the two variables being tested. It provides a way to quantify the difference between the means of the two groups. It can help readers better understand the practical significance of the results. Cohen’s d is a standardized measure of effect size , representing the difference between the means of the two groups in terms of standard deviations. To calculate Cohen’s d, you will need to know each group’s mean and standard deviation.
