T-Test
Candlefocus EditorThe independent t-test determines whether the mean of two separate samples are statistically different, while the dependent t-test compares the means of two related or dependent samples. The t-test is a popular method used in hypothesis testing. It enables researchers to compare a sample mean to a population or hypothetical mean, to test whether a hypothesis is statistically significant or not.
The t-test allows researchers to make statistical inferences about the degree of difference or relationship between two or more samples. It helps researchers decide whether the difference between two samples is likely due to chance or if the samples are actually different. It also helps researchers draw conclusions about the results of the study, enabling them to decide whether a statistically significant effect has occurred.
The calculation for the t-test requires the difference between the means of two data values, the standard deviation of each group, and the number of data values. The t-test can also assess whether the confidence interval for the difference between the two samples includes zero. If the confidence interval does NOT include zero, then the difference is considered to be statistically significant.
The results of a t-test provide an approximate measure of the likelihood of the difference between two samples being due to random chance alone. T-tests are used in a wide variety of research areas to assess the significance of differences or relationships between two or more variables. They can be used to compare the means of two sets of data, the differences between two sets of scores, or to compare the use of different treatments. Ultimately, the t-test allows researchers to make informed conclusions about the results of a study.