Who explains equal variance vs unequal variance t-tests?
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T-tests are statistical tests used to determine whether two samples have statistically different means. The null hypothesis is usually the sample mean of one group is equal to the sample mean of the other group, and the alternative hypothesis is the opposite: the sample mean of one group is greater than the sample mean of the other group. A common situation where this happens is when you have two groups of data from different populations. Let’s look at an example. Suppose you have two groups of 10 subjects, each with mean scores in their two variables. One group is 23 years old
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Who explains equal variance vs unequal variance t-tests? Sure. I explain this in the text, in the next section. But, let me give you a brief overview, before you read this section. The t-test is used to compare the two groups’ means. In case the difference is not significant, then we do a t-test to compare the means (equal variance). This is explained better when you read the next section. The t-test compares the difference in means (the sample mean difference)
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“In regression analysis, we often encounter the challenge of testing for whether a dependent variable is (easily) related to multiple independent variables. In most cases, this is easier than it sounds because we have some degree of freedom in the dependent variable. This is where t-tests come in. T-tests are widely used to test the relationship between two or more independent variables. For example, I once had to perform a t-test to test if the salaries of employees at my workplace differed statistically (in a normal distribution). The normality assumption is valid for most
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I know that the title of this post already has the answer. But, I’d like you to know for sure. That is, who explains equal variance vs unequal variance t-tests. In case you don’t know, a t-test is a statistical test used to compare the means of two groups of data. The two types of t-tests (i.e. T-tests with equal variance vs unequal variance) differ in the way they approach the question of whether the difference between the means is significant. Here’s what you need to
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Both equal variance and unequal variance t-tests are used to compare groups within a subject. Here is the logic: Equal variance t-tests are used when one sample size is relatively more than two times the other and the mean differences between two groups are the same. So, the t-test assumes the distribution of the sample mean to be Gaussian. The mean differences are added for both the groups to get the total amount of variance. These t-tests are used to compare means, which is equivalent to comparing the median of two samples. Here is the
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If the variables in your hypothesis are normally distributed, then the results of your t-test can be compared with an equally-sized alternative hypothesis. That is, whether or not the values in your X- and Y-variables are normally distributed can be compared with the value of the alternative hypothesis that your study is testing. Clicking Here Thus, if the mean difference of the two samples is equal to the standard error of the difference between means, then the two hypothesis tests are equivalent. In such a case, the t-test will be called an equal variance t-test (aka un
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Almost everyone understands the two types of tests: t-tests and chi-square tests, which have both advantages and limitations. However, there are cases where the null hypothesis is not accepted. In these situations, you may use the t-test instead of the chi-square test. This difference can be significant, so knowing which test to choose is a big task for researchers. I will explore the two main types of tests and help students in making informed decisions about which t-test to choose in their research project. What is a t-test?
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Throughout the course of the semester, you’ll encounter many statistical tests. One test that you’ll encounter frequently is called a t-test, which compares a sample mean to the population mean. This test measures whether the sample mean is greater than or less than the population mean. A significant difference between the two values indicates that the sample does not fit the population distribution. An equal variance t-test compares the sample mean to the population mean. Unlike a t-test, the sample mean is unaffected by the size of the sample or