With 20 hours allocated for the IA and a lot to get done, I only have time in my course to plan one lesson for inferential statistics. In this time I want students to get a basic understanding of:
- how inferential stats differ to descriptive ones
- how to choose which inferential statistical test to use
- and most importantly, why inferential statistical tests are applied to data
One really simple way of achieving 1 and 3 is by doing the following quick activity.
You can find the lesson plans, workbooks, powerpoints and other resources for this lesson in our teacher support packs.
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Activity
I show students two sets of data (see the video) with the calculated mean. The example I use in the video is of an experiment about “Rememberol,” a fictional memory improvement drug. (I like to use non-IA examples so they have to think for themselves how to apply the learning to their own studies). From the descriptive statistics you can see that the mean for one condition is higher than the other. I get students to draw a conclusion based on this data.
This video helps students understand why we conduct inferential statistics.
They’ll naturally conclude that Rememberol helps with memory. But then I show them the next slide with data from the control group highlighted: you can see (on the slideshow in the video) that they actually had higher test scores than the treatment group. Now I can challenge students to analyse the data and to see if they would trust these conclusions when 3/7 of the participants’ scores are not consistent with their original conclusion. We might discuss the implications of conclusions in experiments like drug trials: if you were experimenting a new drug that treated, let’s say Parkinson’s Disease, would you allow it to be manufactured and sold with these results?
And now it should be rather straightforward to explain to students the purpose of inferential statistics: they enable us to statistically calculate whether or not we can “trust” our descriptive statistics. Researchers don’t have to analyse the data just by looking, as we’ve just done, because in most quantitative studies there are far more than 7 people and sometimes in the 1,000s, so this would be impossible. We apply the inferential tests to see if our results were actually a product of the manipulation of the IV or if there’s a good probability that they were simply by chance (the technical way of saying it is “we see the likelihood that we would get a given chance if our null hypothesis is true, which is a fancy way of saying we’re seeing if our results were a fluke).
And so this brief activity in about 20 minutes can help students develop what I think is the most important thing for them to take away from this part of the IA: the relationship between statistical significance and inferential statistical tests.
You can find more resources for the IA in our teacher support pack.
Travis Dixon is an IB Psychology teacher, author, workshop leader, examiner and IA moderator.