### Explanation**What an Independent T-Test Does** The independent t-test is an inferential test designed to tell us whether we should accept or reject our null hypothesis. You have learned that any two samples from the same population are unlikely to have the same mean. If you carry out an experiment or collect data from two samples because you expect to see a difference between them, you have a problem because there will almost always be some difference due to sampling! It is vital to know whether the difference between the means of your two samples is due to the effect of sampling or to a true difference between the populations they were sampled from.The independent t-test answers this question. It tells you whether the difference that you have found is due to sampling or a true difference between the populations. **When to use an Independent T-Test** You use an independent t-test when you want to compare the mean of one sample with the mean of another sample to see if there is a statistically significant difference between the two. As the name suggests, you use an independent t-test when your samples are independent! There is more on this topic on the pages about choosing a t-test.
**How to use an Independent T-Test** This level assumes that you will use a software package to perform a t-test. If you want to know how to do it by hand, read level two. The result of using a t-test is that you know how likely it is that the difference between your sample means is due to sampling error. This is presented as a probability and is called a *p-value*. The *p-value* tells you the probability of seeing the difference you found (or larger) in two random samples if there is really no difference in the population.
Generally, if this *p-value* is below 0.05 (5%), you can reject the null hypothesis and conclude that there is a statistically significant difference between the two population means. If you want to be particularly strict, you can decide that the *p-value* should be below 0.01 (1%). The level of *p* that you choose is called the **significance level** of the test. The p-value is calculated by first using the t-test formula to produce a t-value. This t-value is then converted to a probability either by software or by looking it up in a t-table. The next topic covers this part of the process. |