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Do you ever look at your data and say, “huh?” The Unusual Statistical Phenomena of Simp

Sometimes when looking at the results from survey data, we see something that makes us say “huh?” or “that doesn’t look right”.  When the odd results persist after verifying the data were processed correctly (always a good practice), there is typically still a logical answer that can be uncovered after doing some digging.  Sometimes the answer lies with something that we will call “unusual statistical phenomena.”  This is part 1 of a series that will look at some of these interesting – or confounding – effects that do pop up now and then in real survey research data.

This time we will look at Simpson’s Paradox.  And we aren’t referring to the fact that Bart Simpson never seems to age while the rest of us do.  It is actually a phenomenon first described by the statistician Edward H. Simpson in 1951.

It’s easiest to understand this phenomenon through an example.  So, let’s say that we have two ads that have been on air, ad A and ad B.  In our tracking survey among adults 18 to 65, we will ask respondents if they recognize having seen each ad on air.  Earlier in the survey we ask Purchase Intent for the product which is featured in each of the two ads.  From these results, we will compare Top Box Purchase Intent among respondents who recognized each of the two ads.  The results in the table below show somewhat higher Top Box Purchase Intent for Ad A:

However, the client is also interested in seeing the results among each of two age groups: age 18 to 39 and age 40 to 65.  When we table those results, we find something that just doesn’t make sense.  Purchase Intent is slightly higher for Ad B among both age groups – a reversal from the overall results.  How can that be!

After verifying with data processing that the data are correct, we have our team dig into the data to figure out what is going on.  Finally, an explanation is found.

Ad B was aired heavily among programming targeted to a younger audience, while Ad A was primarily aired in general interest programming – which skews to a slightly older audience.  Hence Ad B had much higher recognition among the younger age group – and as a result, a much higher proportion of young people in the set of respondents among whom purchase intent was calculated.

The table of base sizes shown below reveals this imbalance. When combined with the younger age group’s more skeptical nature (and lower results) when it comes to Purchase Intent – especially in our category – the apparent anomaly is explained.

This is an example of Simpson’s Paradox.  It is a phenomenon in which individual subgroups all show the same trend in results, but the trend reverses when the subgroups are combined.  This occurs when there is a confounding variable that causes an imbalance in base sizes such as we saw above.  In our example, the confounding variable was the differing recognition levels for the ads among the two age groups.

Simpson’s paradox shows us the importance of knowing and understanding our data and keeping a watch out for the kind of confounding factors that could end up misleading us if we don’t account for them.

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