Student's t distribution: one-line explanation of its origin

**They say**: *We’ll now learn about a confidence interval that applies even for small sample sizes*… Suppose we knew the standard deviation, , of the sample mean. Then, with the additional assumption that the population is normal, with small we could use the formula , for instance with for 95% confidence. In practice, we don’t know the population standard deviation σ. Substituting the sample standard deviation s for σ to get then introduces extra error. This error can be sizeable when is small. *To account for this increased error, we must replace the z-score by a slightly larger score, called a t-score. The confidence interval is then a bit wider*. (Agresti and Franklin, p.369)

**I say**: The opening statement in italic (*We’ll now learn about...*) creates the wrong impression that the task at hand is to address small sample sizes. The next part in italic (*To account for this increased error...*) confuses the reader further by implying that

1) using the sample standard deviation instead of the population standard deviation and

2) replacing the z score by the t score

are two separate acts. They are not: see equation (3) below. The last proposition in italic (*The confidence interval is then a bit wider*) is true. It confused me to the extent that I made a wrong remark in the first version of this post, see Remark 4 below.

### Preliminaries

William Gosset published his result under a pseudonym ("Student"), and that result was modified by Ronald Fisher to what we know now as Student's t distribution. Gosset with his statistic wanted to address small sample sizes. The modern explanation is different: the t statistic arises from replacing the unknown population variance by its estimator, the sample variance, and it works regardless of the sample size. If we take a couple of facts on trust, the explanation will be just a one-line formula.

Let be a sample of independent observations from a normal population.

**Fact 1**. The z-score of the sample mean

(1)

is a standard normal variable.

**Fact 2**. The sample variance upon scaling becomes a chi-square variable. More precisely, the variable

(2)

is a chi-square with degrees of freedom.

**Fact 3**. The variables in (1) and (2) are independent.

### Intuitive introduction to t distribution

When a population parameter is unknown, replace it by its estimator. Following this general statistical idea, in the situation when is unknown, instead of (1) consider

(3) (dividing and multiplying by ) (using (1), (2))

By definition and because the numerator and denominator are independent, the last expression is a t distribution with degrees of freedom. This is all there is to it.

### Concluding remarks

**Remark 1**. When I give the definitions of chi-square, t statistic and F statistic, my students are often surprised. This is because there is no reference to samples. To be precise, it is better to remember that the way I define them, they are random variables and not statistics and using "distribution" or "variable" would be more appropriate than "statistic". A **statistic**, by definition, is a function of observations. The variable we start with in (3) is, obviously, a statistic. Equation (3) means that that statistic is distributed as t with n-1 degrees of freedom.

**Remark 2**. Many AP Stats books claim that a sum of normal variables is normal. In fact, for this to be true we need independence of the summands. Under our assumption of independent observations and normality, the sum is normal. The variable in (1) is normal as a linear transformation of this sum. Since its mean is zero and its variance is 1, it is a standard normal. We have proved Fact 1. The proofs of Facts 2 and 3 are much more complex.

**Remark 3**. The t statistic is not used for large samples not because it does not work for large n but because for large n it is close to the z score.

**Remark 4**. Taking the t distribution defined here as a **standard t**, we can define a **general t** as its linear transformation,

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