Understanding Independent Samples t-Test
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Understanding Independent Samples t-Test
Understanding Independent Samples t-Test
Exercise 16
Understanding Independent Samples t-Test
Statistical Technique in Review
The independent samples t-test is a parametric statistical technique used to determine significant differences between the scores obtained from two samples or groups. Since the t-test is considered fairly easy to calculate, researchers often use it in determining differences between two groups. The t-test examines the differences between the means of the two groups in a study and adjusts that difference for the variability (computed by the standard
error) among the data. When interpreting the results of t-tests, the larger the calculated t ratio, in absolute value, the greater the difference between the two groups. The significance of a t ratio can be determined by comparison
with the critical values in a statistical table for the t distribution using the degrees of freedom (df) for the study (see Appendix A Critical Values for Student’s t Distribution at the back of this text). The formula for df for an
independent t-test is as follows:
df=(numberofsubjectsinsample1+numberofsubjectsinsample2)−2
Exampledf=(65insample1+67insample2)−2=132−2=130
The t-test should be conducted only once to examine differences between two groups in a study, because conducting multiple t-tests on study data can result in an inflated Type 1 error rate. A Type I error occurs when the researcher rejects the null hypothesis when it is in actuality true. Researchers need to consider other statistical analysis options for their study data rather than conducting multiple t-tests. However, if multiple t-tests are conducted,
researchers can perform a Bonferroni procedure or more conservative post hoc tests like Tukey’s honestly significant difference (HSD), Student-Newman-Keuls, or Scheffé test to reduce the risk of a Type I error. Only the Bonferroni procedure is covered in this text; details about the other, more stringent post hoc tests can be found in Plichta and Kelvin (2013) and Zar (2010).
The Bonferroni procedure is a simple calculation in which the alpha is divided by the number of t-tests conducted on different aspects of the study data. The resulting number is used as the alpha or level of significance for each of the t-tests conducted. The Bonferroni procedure formula is as follows: alpha (α) ÷ number of t-tests performed on study data = more stringent study α to determine the significance of study results. For example, if a study’s α was
set at 0.05 and the researcher planned on conducting five t-tests on the study data, the α would be divided by the five t-tests (0.05 ÷ 5 = 0.01), with a resulting α of 0.01 to be used to determine significant differences in the study.
The t-test for independent samples or groups includes the following assumptions:
- The raw scores in the population are normally distributed.
- The dependent variable(s) is(are) measured at the interval or ratio levels.
Understanding Independent Samples t-Test
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