# Sampling Distribution of r

Site: | Saylor Academy |

Course: | MA121: Introduction to Statistics |

Book: | Sampling Distribution of r |

Printed by: | Guest user |

Date: | Tuesday, August 6, 2024, 11:26 PM |

## Sampling Distribution of Pearson's r

### Learning Objectives

1. State how the shape of the sampling distribution of deviates from normality

2. Transform to '

3. Compute the standard error of '

4. Calculate the probability of obtaining an above a specified value

Assume that the correlation between quantitative and verbal SAT scores in a given population is . In other words, . If students were sampled randomly, the sample correlation, , would not be exactly equal to . Naturally different samples of students would yield different values of . The distribution of values of after repeated samples of students is the sampling distribution of .

The shape of the sampling distribution of for the above example is shown in Figure 1. You can see that the sampling distribution is not symmetric: it is negatively skewed. The reason for the skew is that cannot take on values greater than and therefore the distribution cannot extend as far in the positive direction as it can in the negative direction. The greater the value of , the more pronounced the skew.

**Figure 1.** The sampling distribution of for and .

Figure 2 shows the sampling distribution for . This distribution has a very short positive tail and a long negative tail.

**Figure 2.** The sampling distribution of for and .

Referring back to the SAT example, suppose you wanted to know the probability that in a sample of students, the sample value of would be or higher. You might think that all you would need to know to compute this probability is the mean and standard error of the sampling distribution of . However, since the sampling distribution is not normal, you would still not be able to solve the problem. Fortunately, the statistician Fisher developed a way to transform to a variable that is normally distributed with a known standard error. The variable is called ' and the formula for the transformation is given below.

The details of the formula are not important here since normally you will use either a table or calculator to do the transformation. What is important is that ' is normally distributed and has a standard error of

where is the number of pairs of scores.

Let's return to the question of determining the probability of getting a sample correlation of or above in a sample of from a population with a correlation of . The first step is to convert both and to their ' values, which are and , respectively. The standard error of ' for is Therefore the question is reduced to the following: given a normal distribution with a mean of and a standard deviation of , what is the probability of obtaining a value of or higher? The answer can be found directly from the applet "Calculate Area for a given " to be . Alternatively, you could use the formula:

and use a table to find that the area above is .

Source: David M. Lane, https://onlinestatbook.com/2/sampling_distributions/samp_dist_r.html

This work is in the Public Domain.

### Video

### Answers

- Skewed- Unless , the sampling distribution is skewed. The reason for the
skew is that cannot take on values greater than or less than ,
and therefore the distribution cannot extend as far in one direction as
it can in the other.
- Use a calculator or table to transform to '. You get .
- Although you could plug all of these values into the to ' calculator,
you don't need to do that.
You know the with the biggest absolute value has the most skewed
sampling distribution, so it is the most different from its
corresponding '.
- Population correlation = to ' = , SE = =
= , = to ' = , Use the "Calculate area for a
given " applet. Plug in for the mean and for the SD, and then
calculate area above . You get .