How beer influenced statistics

guinness.jpgBack in the early 1900s a certain W.S. Gosset, an Englishmen, was tasked with brewing better beer.  Really, I’m being serious. 

Gosset was a bright man, with two degrees from Oxford, and was hired by Guinness to help them brew the best beer using statistical methods instead of the “tribal knowledge” approach most brewing companies employed at the time.

Gosset’s main issue

Gosset’s main issue was sample sizes.  At the time the Z-test was the most prevalent test available for someone who wanted to compare a sample mean to some known target, or hypothesized mean.

The problem with the Z-test was it used the population standard deviation (s) which required large sample sizes.  This was impossible for Gosset as his resources were limited for economical reasons.  I mean there is only so much beer allowed for experimentation… wink, wink.

For example, in one experiment Gosset was attempting to determine the optimal type of barley to use.  Initially, these experiments started with 4 farms each growing one plot of each variety.  So assuming he knew the “population” standard deviation from this small sample was dangerous and wrong.  Lucky for all of us, Gosset challenged the status quo and went against the teachings of many well known statisticians of his time.

Introducing the one sample t-test

Gosset’s big, and somewhat controversial move, was to tweak the Z-test ever so slightly, creating what we now call the one sample t-test.  The tweak dealt with using the “sample” standard deviation (s) instead of the “population” standard deviation (s).  There are some mathematical reasons for this related to the fatness of the tails and other fun stuff but I will spare you from this discussion.  I’ll point to some additional reading at the end if you wish to read more.

The benefits

With the one sample t-test Gosset was now able compare a sample mean to some hypothesized mean (target).  And most importantly he did not have to worry about his small sample sizes.

Does it really matter?

A fair question, when comparing the Z-test with the one sample t-test, is does it really matter?  The best answer I can offer is – it depends.

If you are dealing with large sample sizes the results from a Z-test and one sample t-test are likely to be close to one another in which case it doesn’t really matter as much as some purists may say.

However, if you are dealing with smaller sample sizes the one sample t-test is likely the best choice due to the whole standard deviation conundrum.

Next up

Tomorrow night I will discuss the rubrics of the one sample t-test.  There are some assumptions we need to satisfy as well as some tricks we can play making this hypothesis test extremely powerful for both lean and six sigma practitioners alike.

Additional Reading

  1. “Student” by R.A. Fisher
  2. Guinness, Gosset, Fisher, and Small Samples by Joan Fisher Box
  3. Everything you wanted to know about the one-sample t-test (but were afraid to ask) by Keith Bower

Photo Credit: 1

Comments

  1. Duane Roberts says:

    I just came across your website and appreciate the effort you put into this. I personally struggle with many of these stats concepts and you make them easier to understand meaning you speak English and not the stuff my prof rambled on about for a semester!

  2. Great stuff.

  3. Thank you Duane and John. I appreciate the kind words.

  4. What does stand for the t in the t-test
    and what does mean the Z in the z-test?

  5. Jervy Redolfin says:

    I appreciated your effort on doing this brief history. I easily understand it, but I don’t understand why they call it t-test, I mean what is “t” on t-test ?

    • Fisher chose the letter “t” as a simple way to refer to Gosset’s Student distribution. There doesn’t seem to be anything more to it than this… if there is I have not heard about it! :-)

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