Researchers should not compare artificially created groups in an experiment (e.g. based on the median). There are several problems with median-splits:Median splits change the original information drasticallyEffect sizes get smallerThere is an increased chance of finding spurious effectsCATEGORICAL PREDICTORS IN THE LINEAR MODELComparing the difference between the means of two groups is predicting an outcome based on membership of two groups. A t-statistic is used to ascertain whether a model parameter is equal to zero. In other words, the t-statistic tests whether the difference between group means is equal to zero.THE T-TESTThere are two types of t-tests:Independent t-test (independent measures t-test)This is comparing two means in which each group has its own set of participants.Paired-samples t-test (dependent t-test)This is comparing two means in which each group uses the same participants.The t-test is used to see whether there is an actual difference between two groups (e.g. experimental and control). If there is no difference between the two groups, we expect to see the same mean. There is natural variation in each sample, so the mean is (almost) never exactly the same. Therefore, just by looking at the means, it is impossible to state whether there is a significant difference between two groups. In the t-test, a set level of confidence (normally 0.95), alpha, is used as a threshold of when the difference is significant. The t-statistic is used to compute a p-value and this p-value is compared to the alpha. If the p-value is equal to or smaller than the alpha, it means that there is a significant difference between the two means and then we state that there is an actual difference. The larger the difference between two means relative to the standard error, the more likely it is that there is an actual difference between the two means. The t-test is always computed under the assumption that the null hypothesis is true. It...