T-Test in Excel (NEW VERSION IN DESCRIPTION)

NEW VERSION
T test p Value in Excel
• T test p Value in Excel
This video shows Dr. Evan Matthews explaining how to calculate a t-test in Excel, and how to interpret those results. The sample data in this video represents the difference between standing and supine (laying on back) heart rate in beats per minute.
Link to video of how to perform averages in Excel, and how to do a simple bar graph in Excel.
• Basic Bar Graph in Excel
Link to video showing how to improve the appearance of this graph to mimic the appearance of graphs typical in physiology research papers.
• Publication Quality Ba...
Link to video of how to perform a correlation in Excel.
• Correlation Statistics...
Excel Skills Playlist
• Excel Microsoft Office
Link to Dr. Evan Matthews website.
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[@juded14]

For people like myself, your explanation of each argument, the meaning and the examples help the viewers follow what you're doing. So thank you for the thorough explanation.

[@VivoPhys]

+Jude Diegue I'm happy the video was helpful to you!

[@waynegreaves9070]

@@VivoPhys Your Null Hypothesis is Mu1 = Mu2. Your Alternate Hypothesis is Mu1 is not equal to Mu2. After calculating your t-test which for me came out to be 0.003735, using all the exact parameters you used (In any event, let's not dwell on the t-test calculation), you then need to compare your t-test to your t-Critical. Looking up your t-Critical for the 5% significant level (0.05) you will have gotten a t-Critical of 2.074 which, in any event, is still higher than the calculated t-test. As the mnemonic goes: If the p-value is high, the Null must fly, if the p-value is low, the Null must go. Both of our p-value, i.e., t-Critical is high which means the Null must fly, i.e., you must fail to reject the Null Hypothesis: Mu1 = Mu2 at the 95% Confidence Level, meaning there is NO STATISTICAL SIGNIFICANT difference between the Population Means: Mu1 = Mu2. You don't compare your t-test to 0.05. 0.05 (5%) is simply the level of significance you chose. You compare your t-test to your t-Critical value which takes the additional step of finding you degrees of freedom (df) which in this case turns out to be 22 (n1+n2-2) and looking up the t-Critical either on an APP or in a stat table.

[@VivoPhys]

Thanks for posting this more in depth method.

[@mariahenriquez2468]

Thank you so much! Your video explained to me exactly what I needed to understand for my physiology class! My professors didn't cover this and I was soooooooo lost. Thank you!

[@VivoPhys]

You're welcome Maria Henriquez!

[@Courtc2]

This was so helpful, thank you!

[@VivoPhys]

I'm happy it helped.

[@nihonbunka]

Thank you. I like the way that you put yourself in front of the excel spreadsheet. I was taught that if you had a clear and justified hypothesis (in this case that people in a supine position would have a lower heart rate due to their increased relaxation and less of a requirement to pump blood against the force of gravity) then it is justifiable to use a one tailed test, which gives half the p value, AFAIK and makes twice as easy to reach statistical significance. If the test could go either way then one should use a two tail test. But I may well be wrong. And I did not even know about "type 3"! I just used type two when the subjects are different. I wonder if one should check the variance or standard deviation (=stdev(array)) of the two groups of subjects, and how much the standard deviation would need to deviate for the two groups to be considered to have different variance or, all other things (other than the independent variable) being equal, one can assume that the variance is the same.

[@jakemarshall3406]

This helped a lot. Cheers.

[@VivoPhys]

Jake Marshall I'm happy it helped!

[@nisakornyodsanit854]

Thanks so much!

[@VivoPhys]

I'm glad it helped.

[@lulu-og1ho]

thanks you saved me from 10 minutes opening of MATLAB

[@VivoPhys]

I'm glad it helped.

[@lucasdelmonico]

Thank you so much!!!

[@VivoPhys]

You're welcome Lucas Delmonico!