What Are t-Tests? - Assessing Statistical Differences Between Groups

What Are t-Tests? - Assessing Statistical Differences Between Groups
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  • 0:07 Definitions
  • 1:41 Null & Alt
  • 2:38 t-Test Stats
  • 6:54 Lesson Summary
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Lesson Transcript
Instructor: Devin Kowalczyk

Devin has taught psychology and has a master's degree in clinical forensic psychology. He is working on his PhD.

This lesson explores how a researcher may use a t-Test. In addition, simple to follow instructions will demonstrate how to manually complete a t-Test.


I've often wondered if having two classes combined is a good thing. No. Sorry. Not some kind of anti-Marxist/Marxist idea - I meant school classes. When you combine two classes you have to teach to the lowest level in the room. For example, if you had a class for high schoolers where you combined seniors and freshman, than you have to teach it at the freshman level. This will bore the seniors out of their minds because they are well above that level.

However, if you taught at the senior level, then you would confuse the freshman because they haven't had as much experience. The same issues happen at college and graduate school, probably even more. So the question for those designing the classes and those who are evaluating whether or not to combine two different levels of students is, 'Are the experience and knowledge levels of the classes so different that one group will not get any use out of the class?'

The t-Test is a statistical test to determine if there is a measured difference between two groups. The t-Test always has a lower case letter 't,' which seems to have been chosen arbitrarily. The t-Test can also be referred to as the Student's t-Test because it was printed under the pseudonym 'Student' by William Gossett to avoid issues with an employer at a brewery. While you don't need to know this to do the statistics, sometimes it's interesting to remember that the purpose of statistics is to solve real world problems. For instance, is beer A, on average, better than beer B?

Null & Alt

t-Tests use both a null and an alternative hypothesis. A null hypothesis is typically the standard assumption and is defined as the prediction that there is no interaction between the variables. This is opposed by the alternative hypothesis, also known as the research hypothesis, defined as the prediction that there is a measured interaction between the variables.

Because t-Tests are specifically looking to see if there is a difference between the two groups of scores, the null hypothesis is that there is no difference between the groups. So, if we have a 4th and 5th grade combination class and if we failed to reject the null hypothesis, then that would mean there is no significant difference between the 4th and the 5th graders. The t-Test can reject the null hypothesis, which would mean that we would look to the alternative hypothesis. With the alternative hypothesis, we would be able to say that there is a significant difference between the groups.

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