### If your stats class was anything like mine, you learned that using ANOVA instead of t-tests is a sneaky way to avoid the multiple testing problem. I still believed this until very recently and a lot of my colleagues are bewildered, when I try to convince them otherwise. Of course this has been covered quite a bit. For example, there is an awesome blog-post by Dorothy Bishop and there is this great recent paper . In addition to that, today, I want to share a simple way to convince yourself this truly is a problem using some simulated data and SPSS.

Let’s say you have run a simple experiment on intelligence, where you wanted to find out, if a drug enhances problem solving capacity. You ran two experimental days, one day you gave placebo the other day you gave the treatment. On each experimental day you first ran your IQ-test for a baseline measurement and then gave the participant either the treatment or a placebo. When the drug was ingested you tested IQ again, assuming that an increase would happen only in the treatment condition. After running 50 participants, you get the data plotted above. You open SPSS and run a repeated measures ANOVA defining your first factor as a treatment factor with the levels treatment vs. placebo and your second factor as a time point factor with the levels before vs. after. In your output below you find no significant main effect of treatment (p = 0.246), no main effect of time point (p = 0.062), but as you predicted there is an interaction between treatment and time point (p = 0.026). You publish this in Science and get tenure.

However, there is an alternate way of getting p-values for these data. Instead of running a repeated measures ANOVA you can do some addition or subtraction to get rid of one factor and then use repeated measures t-tests to compare the remaining conditions. For example, to analyse a treatment effect you add the before and after values from the treatment and the placebo condition, respectively, and compare them. Which works similarly for the time point effect. For the interaction you have to deduct the before condition from the after condition for both treatment conditions and then compare. Below is the output of this approach and it may surprise you that you get exactly the same p-values (0.246, 0.062, 0.026) as from the ANOVA. If you Bonferroni correct these none of them remain significant.

For me this approach was an eye-opener, as in your first stats lesson you learn that running three t-tests means you must correct for multiple comparisons. Since you get the same p-values running ANOVA, it obviously does not magically self-correct for this problem and you have to manually correct as for t-tests. I have added my simulated data and my SPSS-script so you can try this out yourself (you can also check out my MATLAB code). Once you have convinced yourself, do also check out the resources in the intro to understand why this is the case. Importantly, this problem goes away, if you preregister the interaction as your effect of interest, but then strictly you must not calculate the main effects. Please let me know, what you think about this little demonstration in the comments.

MATLAB Code

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clear all rng(20202222, 'Twister') %here we set the amount of subjects subs = 50; %here we generate some random numbers as ground truth, e.g. IQ mean_t = 100; SD_t = 15; truth = normrnd(mean_t,SD_t,subs,1); %here we set the measurement error. the higher it is the lower %retest-reliability error_variance = 8; %effect size (ratio of variance and mean is effect size) effect_variance = 10; mean_effect = 5; %Here we generate data from our "Experiment" with 2 repeated measures %factors, e.g. treatment vs placebo and before vs. after data(:,1) = truth+normrnd(0,error_variance,subs,1); data(:,2)= truth+normrnd(0,error_variance,subs,1)... +normrnd(mean_effect,effect_variance,subs,1); data(:,3)= truth+normrnd(0,error_variance,subs,1); data(:,4)= truth+normrnd(0,error_variance,subs,1); %we sum some of the data for t-tests data(:,5)=sum(data(:,1:2),2); data(:,6)=sum(data(:,3:4),2); data(:,7)=sum(data(:,[1 3]),2); data(:,8)=sum(data(:,[2 4]),2); data(:,9)= data(:,2)-data(:,1); data(:,10)= data(:,4)-data(:,3); data(:,11)=sum(data(:,1:4),2); %quick t-test to see if there is a significant difference [h,p] = ttest(data(:,1),data(:,2)); %here you can check the correlations, you should aim for something %realistic reliability = corrcoef(data(:,1:4)); %create variable name header for SPSS Header = {'before_treatment' 'after_treatment' 'before_placebo' ... 'after_placebo' 'treatment' 'placebo' 'before' 'after' ... 'change_treatment' 'change_placebo' 'all_data'}; %header textHeader = strjoin(Header, ','); %write header to file filename = 'ANOVA_blog_data.csv'; fid = fopen(filename,'w'); fprintf(fid,'%s\n',textHeader) fclose(fid) %write data to end of file dlmwrite(filename,data,'-append');

[/codesyntax]

SPSS syntax

PRESERVE. SET DECIMAL DOT. GET DATA /TYPE=TXT /FILE="...\ANOVA_blog_data.csv" /ENCODING='UTF8' /DELIMITERS="," /QUALIFIER='"' /ARRANGEMENT=DELIMITED /FIRSTCASE=2 /DATATYPEMIN PERCENTAGE=95.0 /VARIABLES= before_treatment AUTO after_treatment AUTO before_placebo AUTO after_placebo AUTO treatment AUTO placebo AUTO before AUTO after AUTO change_treatment AUTO change_placebo AUTO @all AUTO /MAP. RESTORE. CACHE. EXECUTE. DATASET NAME DataSet3 WINDOW=FRONT. GLM before_treatment after_treatment before_placebo after_placebo /WSFACTOR=treatment_factor 2 Polynomial timepoint_factor 2 Polynomial /METHOD=SSTYPE(3) /CRITERIA=ALPHA(.05) /WSDESIGN=treatment_factor timepoint_factor treatment_factor*timepoint_factor. DATASET ACTIVATE DataSet3. T-TEST PAIRS=treatment before change_treatment WITH placebo after change_placebo (PAIRED) /CRITERIA=CI(.9500) /MISSING=ANALYSIS.

Hi Gordon, thanks for your post! I’ve written a blog post approaching these issues from a regression framework with some annotated Stata output. It’s here: https://renebekkers.wordpress.com/2018/08/04/multiple-comparisons-in-a-regression-framework/

Hey Gordon,

you’re making a good point but I still have to disagree with you a bit. At least in my class the self-correction property was aimed at comparing multiple groups. So if you had three different conditions and your question would be wether or not there are differences between these conditions (assuming just one main effect here) – without asking specifically between which groups these differences are, you wouldn’t have to correct for three comparisons using an ANOVA. But you would have to correct if you computed three t-tests. That’s another context of multiple comparisons than the one you’re talking about.

Hi Lukas,

thanks for your comment! Not sure I get your point. If you mean a one-way ANOVA, you are completely correct. This allows you to test two or more levels of one factor without the need of correction. My example was a 2×2 ANOVA and here, if you can interpret the main effect or the interaction effect as support for your hypothesis then you would have to correct. This seems unlikely in my example, but if you think of treatment by sex interactions this occurs more often.

Best,

Gordon

Hi Gordon,

Thanks for this post! I found this quite convincing, but when I talk to colleagues about this, I frequently am confronted with the counter-argument that this only applies when you do not have any strong a-priori hypothesis and simply perform an ANOVA to see wether there is “some sort of difference” between your conditions. For instance, Cramer et al (2016, Psychon Bull Rev) argue that this is mainly a problem when conducting an exploratory ANOVA. What’s your take on that?

Hi Laura,

yes I completely agree with this counter argument. If you have preregistered your study then you don’t need to correct. Similarly, no correction for multiple-comparisons is needed, when you preregister an ROI in a neuroimaging study. I did want to add another post to this one giving a more concrete example, but never got around to it. It would go something like this: in an experiment you may be measuring the effect of giving a drug in the morning or the evening. If you are only interested in the drug “working” at one or both time points, you may claim “success” if you either get the interaction or the main effect. Then this type of false positive inflation is important. I think Dorothy Bishop’s example with EEG-positions is also quite convincing in this regard!

But thanks for this clarifying question!