When comparing the means from two different groups of data, T-tests are often us

Question

When comparing the means from two different groups of data, T-tests are often used. They used to help researchers determine if means are different from one another, and to what significance. When means are drastically different, it is said that the variable is manipulated, or that the Independent Variable (IV), had an impact upon the measured variable, or the Dependent Variable (DV).

Independent

When researchers want to compare groups of participants that are not related in any way, independent t-tests are the most appropriate tool.

For example, comparing how much exercise a person who intakes caffeine, to a person who deprives themselves of caffeine.

Paired Samples T-tests

When groups that are related in some way need to be compared, paired sample t-tests are most appropriate.

For example, a mother and daughter are divided into two groups; as are twins, to be compared, requiring a paired samples t-test.

So, let's say I am a pharmaceutical company that wants to issue a new blood pressure management drug. I recruit 250 participants for the study and wnat to compare before and after results to measure the drug's impact. What form of the t-test would I use and why?

Explanation / Answer

The t test to be used here is the paired sample t-tests.

Dependent samples: If you collect two measurements on each item, each person, or each experimental unit, then each pair of observations is closely related, or matched.For example, suppose you test the Quantitative Aptitude of n students before and after they complete an Aptitude course. This will produce a set of paired observations (Before and After test scores) for each participant. In that case, we use the paired t-test to test the mean difference between these dependent observations.

A Paired Design Reduces Experimental Error: A paired difference test uses additional information about the sample that is not present in an ordinary unpaired testing situation, either to increase the statistical power, or to reduce the effects of confounders." -- "increases power" means it reduces the type II error rate; 'reduces the effect of confounders' would reduce the rate of either type of error (Type I or Type II, or maybe even both.

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