Lessons from a Statistical Analysis Gone Wrong, part 1

Minitab Blog Editor 14 July, 2015

I don't like the taste of crow. That's a shame, because I'm about to eat a huge helping of it. 

I'm going to tell you how I messed up an analysis. But in the process, I learned some new lessons and was reminded of some older ones I should remember to apply more carefully. 

This Failure Starts in a Victory

My mistake originated in the 2015 Triple Crown victory of American Pharoah. I'm no racing enthusiast, but I knew this horse had ended almost four decades of Triple Crown disappointments, and that was exciting. I'd never seen a Triple Crown won before. It hadn't happened since 1978. 

So when an acquaintance asked to contribute a guest post to the Minitab Blog that compared American Pharoah with previous Triple Crown contenders, including the record-shattering Secretariat, who took the Triple Crown in 1973, I eagerly accepted. 

In reviewing the post, I checked and replicated the contributor's analysis. It was a fun post, and I was excited about publishing it. But a few days after it went live, I had to remove it: the analysis was not acceptable. 

To explain how I made my mistake, I'll need to review that analysis. 

Comparing American Pharoah and Secretariat

In the post, we used Minitab's statistical software to compare Secretariat's performance to other winners of Triple Crown races. 

Since 1926, the Belmont Stakes has been the longest of the three races at 1.5 miles. The analysis began by charting 89 years of winning horse times: 

Only two data points were outside of the I-chart's control limits:

  • The fastest winner, Secretariat's 1973 time of 144 seconds
  • The slowest winner, High Echelon's 1970 time of 154 seconds

The average winning time was 148.81 seconds, which Secretariat beat by more than 4 seconds. 

Applying a Capability Approach to the Race Data

Next, the analysis approached the data from a capability perspective: Secretariat's time was used as a lower spec limit, and the analysis sought to assess the probability of another horse beating that time. 

The way you assess capability depends on the distribution of your data, and a normality test in Minitab showed this data to be nonnormal

When you run Minitab's normal capability analysis, you can elect to apply the Johnson transformation, which can automatically transform many nonnormal distributions before the capability analysis is performed. This is an extremely convenient feature, but here's where I made my mistake. 

Running the capability analysis with Johnson transformation, using Secretariat's 144-second time as a lower spec limit, produced the following output:

The analysis found a .36% chance of any horse beating Secretariat's time, making it very unlikely indeed. 

The same method was applied to Kentucky Derby and Preakness data. 

We found a 5.54% chance of a horse beating Secretariat's Kentucky Derby time.

We found a 3.5% probability of a horse beating Secretariat's Preakness time.

Despite the billions of dollars and countless time and effort spent trying to make thoroughbred horses faster over the past 43 years, no one has yet beaten “Big Red,” as Secretariat was known. So the analysis indicated that American Pharoah may be a great horse, but he is no Secretariat. 

That conclusion may well be true...but it turns out we can't use this analysis to make that assertion. 

My Mistake Is Discovered, and the Analysis Unravels

Here's where I start chewing those crow feathers. A day or so after sharing the post about American Pharoah, a reader sent the following comment: 

Why does Minitab allow a Johnson Transformation on this data when using Quality Tools > Capability Analysis > Normal > Transform, but does not allow a transformation when using Quality Tools > Johnson Transformation? Or could I be doing something wrong? 

Interesting question. Honestly, it hadn't even occurred to me to try to run the Johnson transformation on the data by itself.

But if the Johnson Transformation worked when performed as part of the capability analysis, it ought to work when applied outside of that analysis, too. 

I suspected the person who asked this question might have just checked a wrong option in the dialog box. So I tried running the Johnson Transformation on the data by itself.

The following note appeared in Minitab's session window: 

no transformation is made

Uh oh.  

Our reader hadn't done anything wrong, but it was looking like I made an error somewhere. But where?

I'll show you exactly where I made my mistake in my next post.  


Photo of American Pharoah used under Creative Commons license 2.0.  Source: Maryland GovPics https://www.flickr.com/people/64018555@N03