Student's t allows the use of a small number of measurements to estimate what may be true of the whole population.  This forms the basis of modern inferential statistics, where a small number of observations are made, and the results are generalized to the wider population.

The t distribution curve is wider than the normal one.  Therefore, a larger area (or higher probability) of being greater than a particular deviate is obtained compared to the normal distribution. This difference varies with sample size (degrees of freedom), such that the probability of t approaches that of z when the sample size increases towards infinity. Conceptually, this is represented by the diagram to the left.

With infinite degrees of freedom (i.e., a large sample size), the one tailed t and z have the same value for a particular probability, but with fewer cases, t will be larger than z in obtaining the same probability. 

A two tailed t however, assumes the area excluded are on both sides (tails) of the t distribution, so that each side contains only half of the excluded area, as shown in the following diagram.

In calculations involving the confidence interval, the two tailed t is usually used.  

For examples :

• For a two tail model calculating 95% confidence interval using a sample of 21 cases, the t value for p=0.05 (p=0.025 excluded on each of the two tails) and degrees of freedom=20 is 2.09 is obtained. In other words, the 95% confidence interval is mean±2.09SD.
• For a one tail model, p=0.05 (all 5% excluded in one tail) the t value is 1.72. The 95% confidence interval is either -∞ to mean+1.72SD, or mean-1.72SD to +∞, depending on which tail is used.

We compare the birth weight of 10 boys and 10 girls, to draw conclusions whether they differed in weight. The results are as follows.