Choosing a Statistical Test for Your IB Biology IA

CORRECTION AT 8:51: in the chart, 'Wilcoxon' and 'Mann Whitney' should be switched. Wilcoxon is the non-parametric version of the PAIRED t-test (not unpaired as the video suggests). Mann Whitney is the non-parametric version of the UNPAIRED t-test (not paired as the video suggests).

One small caveat: in broader mathematics, "number of bacterial colonies" would be treated as a **discrete variable**, which means the variable is numeric but it's restricted to certain values (and between those allowable values are gaps that the variable can't take on). But if you're plugging that variable into a regression or t-test/ANOVA model, then you're treating it as continuous. To quote minitab, which has great articles on statistics:

"If you have a discrete variable and you want to include it in a Regression or ANOVA model, you can decide whether to treat it as a continuous predictor (covariate) or categorical predictor (factor). If the discrete variable has many levels, then it may be best to treat it as a continuous variable. Treating a predictor as a continuous variable implies that a simple linear or polynomial function can adequately describe the relationship between the response and the predictor. When you treat a predictor as a categorical variable, a distinct response value is fit to each level of the variable without regard to the order of the predictor levels. Use this information, in addition to the purpose of your analysis to decide what is best for your situation."

One big caveat: some may take issue with the terms 'relationship' and 'comparison' and the way I'm using them. Consider a medical researcher who is testing a new drug by comparing a treatment group with a placebo group. She might say: "the video says I'm doing a *comparison* of two groups. But I disagree; I believe I'm seeking a relationship/correlation between the drug and the therapeutic effect." So who is right--the video or the medical researcher? The fact is that comparisons can allow us to deduce relationships, and this creates ambiguity. In the video, the term 'relationship' perhaps should be interpreted very narrowly to mean: 'you're seeking a mathematical equation that relates the variables.' And the term 'correlation' should be interpreted narrowly to mean: 'you're seeking a number showing how correlated your two variables are.' These terms (comparison & relationship) describe what you're doing with the data and variables themselves, not the larger goals of the experiment.

Nothing can replace practice; the more you use these tests, the more you'll understand how they apply and what their limitations are. I'm not an expert on statistical tests. If you find other good explanations or sources that go into subtleties that the video overlooks, please share them in the comments!

[danielm]

One small caveat: in broader mathematics, "number of bacterial colonies" would be treated as a *discrete variable*, which means the variable is numeric but it's restricted to certain values (and between those allowable values are gaps that the variable can't take on). But if you're plugging that variable into a regression or t-test/ANOVA model, then you're treating it as continuous. To quote minitab, which has great articles on statistics:

"If you have a discrete variable and you want to include it in a Regression or ANOVA model, you can decide whether to treat it as a continuous predictor (covariate) or categorical predictor (factor). If the discrete variable has many levels, then it may be best to treat it as a continuous variable. Treating a predictor as a continuous variable implies that a simple linear or polynomial function can adequately describe the relationship between the response and the predictor. When you treat a predictor as a categorical variable, a distinct response value is fit to each level of the variable without regard to the order of the predictor levels. Use this information, in addition to the purpose of your analysis to decide what is best for your situation."

[tekmepikcha]

This is perhaps the most simpliest and yet full tutorial I've heard on statistical tests put together Daniel. Thanks

[MCDLXXXVIII]

This is by far the best material about SPSS I've ever seen in my whole life. Throw 100$ SPSS books into trash can. This video is very beautiful in a minimalist way. Greetings from a procrastinator doctor, who is trying to hurry analysis at 07:00 AM for the finish date of a paper :-)

[Tiatabs]

I have all this data I’ve collected for my masters thesis, I’ve been looking for a test to use for two days. This video, hands down, has just saved my life. THANK YOU!!!

[robingriffin]

9:57 minute video vs 6 weeks of lectures...just wow!

[mmmm]

Thanks to Allah.. I have found someone who is teaching the basics..I needed it badly

[gutsandglory]

Man, this guy nailed it. I fumbled in my undergrad research. I wish this was available to me then. Thank you, Sir.

[rodsalomon]

Im from the Linguistics field in this is still helpful for my thesis, thank you!

[Lydia-yolo]

Thank you so much for this video! Simple, concise, well organised -- it's rare to see such a well-made tutorial to a somewhat confusing topic such as this, amazing :)

[danielelieh-ali-komi]

This video includes more data and has better educational content than what I learned in my MSc. Thank you Daniel.

[view]

After struggling for years trying to figure out about the necessity of so many statistical tests, finally I have an overview of statistical tools an how to choose one from.

[asmaaadventures]

Wow! I cannot express how much I am grateful to you for making this video. I spent one week figuring out which suitable statistical test was for my case. THANK YOU

[michalinaprycka]

This video has helped me more than my biology teacher, love you man

[darrenzerone]

Statistics are so simple and easy to understand after your wonderful explanation. Thank you for this amazing video.

[poulami]

I watched so many videos on this topic. But this is the best explanation so far. Thank you so much

[jodieharth]

Thank you! Was recommended for Business Data Analytics.
4:08 is particularly useful if you're wondering what sort of test to use (or, what it's called!) Chi-Squared / t-Test / Correlation.

[v]

I fell in love with the way you explained both the qualitative and quantitative techniques.

[Sarmilagiri]

The most simpliest and useful tutorial i ever heard and seen. Thank you soo much for giving us such a wonderful lessons. 😊😊😊

[saggrawal]

An amazingly simple description of statistical tests. Thank you so much!

[donharris]

For a broad perspective, this is a remarkable video. Nice work! This helps a lot