{"id":9720,"date":"2023-04-27T01:59:59","date_gmt":"2023-04-27T01:59:59","guid":{"rendered":"https:\/\/support.divominer.com\/en\/docs\/divominer-user-manual\/advanced-statistics\/t-test\/"},"modified":"2023-11-08T03:27:12","modified_gmt":"2023-11-08T03:27:12","slug":"t-test","status":"publish","type":"docs","link":"https:\/\/support.divominer.com\/en\/docs\/divominer-user-manual\/advanced-statistics\/t-test\/","title":{"rendered":"t-Test"},"content":{"rendered":"\n

1. t-Test<\/strong><\/p>\n\n\n\n

The t test, also known as the student\u2019s t-test, is a statistical hypothesis test used to determine whether there is a significant difference between the means of two independent samples. It is often used in research and data analysis to test hypotheses about the mean of a small sample drawn from a normally distributed population when the population standard deviation is unknown. The t-test uses the t-distribution theory to infer the probability of a difference, and compares whether the difference between two means is significant. Here are the differences between some of the most common tests of hypothesis:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

2. One-sample, two-sample, and paired t test<\/strong><\/p>\n\n\n\n

There are different types of t test based on whether the groups being compared come from a single population or two different populations: one-sample, two-sample (a.k.a the independent t-test), the paired t-test. If there is one group being compared against a standard value, perform a one-sample t test; the independent samples t-test compares the means of two separate groups, and the paired t-test compares the means of two related groups.<\/p>\n\n\n\n

The one-sample t-test is used to analyze whether the quantitative data (in a normal distribution) is significantly different from a given value, such as comparing the acidity of a liquid to a neutral pH of 7. The formula for the one-sample t-test is:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Where \"\" \"\" is the mean of the sample, \u03bc is the overall mean, \"\"is the standard deviation of the sample, and n is the sample size. The statistic t obeys the t-distribution with n degrees of freedom under the condition that the null hypothesis: \u03bc=\u03bc0 is true.<\/p>\n\n\n\n

The two-sample (independent sample) t-test is used to test the differences in two different populations, such as to test whether there is a significant difference in two different species, or people from two separate cities. The formula for the independent samples t-test is:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Among them, \"\"and \"\" are the two-sample variance, and \"\" and \"\" are the two-sample capacity.<\/p>\n\n\n\n

The paired sample t-test is a within-subjects design often used in experimental research, to test the differences in the data obtained by two groups of data obtained under different conditions. For example, compare whether there is a significant difference in the purchase intention of the sample in the context of advertisements and no advertisements. The formula for the paired t-test is:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

where dbar = mean difference, sd\/\u221an = standard error = standard deviation of the difference \/ sqrt of number of samples.<\/p>\n\n\n\n

3. One-tailed or two-tailed t test<\/strong><\/p>\n\n\n\n

Based on whether you want to test the difference in a specific direction, there is one-tailed t-test and two-tailed t-test. A one-tailed t-test, also known as a directional t-test, is a statistical hypothesis test where the null hypothesis specifies that the mean of the population is either greater than or less than a certain value. Thus, the alternative hypothesis is that the mean is in the opposite direction. One-tailed t-tests are used when the researcher has a clear directional hypothesis or when it is only important to detect differences in one direction. A two-tailed t-test, also known as a non-directional t-test, is a statistical hypothesis test where the null hypothesis specifies that the mean of the population is equal to a certain value. The alternative hypothesis is that the mean is either greater than or less than the null hypothesis value. Two-tailed t-tests are used when the researcher does not have a clear directional hypothesis or when it is important to detect differences in both directions. In practice, the choice of an appropriate t test depends on the research question, the available data, and the hypotheses being tested.<\/p>\n\n\n\n

4. Perform t-Test on DiVoMiner\u00ae<\/strong><\/p>\n\n\n\n

On DiVoMiner\u00ae platform, click \u201cStatistical analysis \u2013 Advanced statistics \u2013 Create a calculation task \u2013 t-Test\u201d. More statistical tests will be released soon. Stay tuned!<\/p>\n\n\n\n

5. References: <\/strong><\/p>\n\n\n\n