# 7.2: A Single Population Mean using the Student t Distribution

- Page ID
- 29610

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## A Single Population Mean using the Student t Distribution

## jkesler

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation *s* as an estimate for *σ* and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.

William S. Goset (1876–1937) of the Guinness brewery in Dublin, Ireland ran into this problem. His experiments with hops and barley produced very few samples. Just replacing *σ* with *s* did not produce accurate results when he tried to calculate a confidence interval. He realized that he could not use a normal distribution for the calculation; he found that the actual distribution depends on the sample size. This problem led him to “discover” what is called the Student’s t-distribution. The name comes from the fact that Gosset wrote under the pen name “Student.”

Up until the mid-1970s, some statisticians used the normal distribution approximation for large sample sizes and used the Student’s t-distribution only for sample sizes of at most 30. With graphing calculators and computers, the practice now is to use the Student’s t-distribution whenever *s* is used as an estimate for *σ*.

If you draw a simple random sample of size *n* from a population that has an approximately normal distribution with mean *μ* and unknown population standard deviation *σ* and calculate the *t*-score $$t=\frac{\bar x – \mu}{s/\sqrt{n}}$$ then the *t*-scores follow a **Student’s t-distribution with n – 1 degrees of freedom**. The

*t*-score has the same interpretation as the

*z*-score. It measures how far $ \bar x$ is from its mean

*μ*. For each sample size

*n*, there is a different Student’s t-distribution.

The degrees of freedom, ** n – 1**, come from the calculation of the sample standard deviation

**. In Figure G1, we used**

*s**n*deviations $(x – \bar{x} \text{ values})$ to calculate

**. Because the sum of the deviations is zero, we can find the last deviation once we know the other**

*s***deviations. The other**

*n*– 1**deviations can change or vary freely.**

*n*– 1**We call the number**

*n*– 1 the degrees of freedom (df).- The graph for the Student’s t-distribution is similar to the standard normal curve.
- The mean for the Student’s t-distribution is zero and the distribution is symmetric about zero.
- The Student’s t-distribution has more probability in its tails than the standard normal distribution because the spread of the t-distribution is greater than the spread of the standard normal. So the graph of the Student’s t-distribution will be thicker in the tails and shorter in the center than the graph of the standard normal distribution.
- The exact shape of the Student’s t-distribution depends on the degrees of freedom. As the degrees of freedom increases, the graph of Student’s t-distribution becomes more like the graph of the standard normal distribution.
- The underlying population of individual observations is assumed to be normally distributed with unknown population mean
*μ*and unknown population standard deviation*σ*. The size of the underlying population is generally not relevant unless it is very small. If it is bell shaped (normal) then the assumption is met and doesn’t need discussion. Random sampling is assumed, but that is a completely separate assumption from normality.

### Google Sheets

Calculators and computers can easily calculate any Student’s t-probabilities. Google Sheets has the `T.DIST`

function (along with `T.DIST.2T`

and `T.DIST.RT`

explained in a later chapter ) to find the probability for given values of *t*. The grammar for the `T.DIST`

command is `T.DIST(x, degrees of freedom, cumulative?)`

. However for confidence intervals, we need to use **inverse** probability to find the value of *t* when we know the probability.

For Google Sheets, you can use the `T.INV`

function. The `T.INV`

function works similarly to the `NORM.INV`

function. The `T.INV`

function requires two inputs: **T.INV(area to the left, degrees of freedom)** The output is the t-score that corresponds to the area we specified.

A probability table for the Student’s t-distribution can also be used. The table gives t-scores that correspond to the confidence level (column) and degrees of freedom (row).

A Student’s t table gives *t*-scores given the degrees of freedom and the right-tailed probability. The table is very limited. **Spreadsheets can easily calculate any Student’s t-probabilities.**

**The notation for the Student’s t-distribution (using**

*T*as the random variable) is:*T ~ t*where_{df}*df*=*n*– 1.- For example, if we have a sample of size
*n*= 20 items, then we calculate the degrees of freedom as*df*=*n*– 1 = 20 – 1 = 19 and we write the distribution as*T ~ t*._{19}

**If the population standard deviation is not known**, the error bound for a population mean is:

- $E = t_{\alpha/2}\cdot\frac{s}{\sqrt{n}}$
- $t_{\alpha/2}$ is the $t$-score with an area to the right equal to $\frac{\alpha}{2}$

Note that the`T.INV`

function in a spreadsheet expects to be given a probability to the left. Since $t_{\alpha/2}$ is the $t$-score with an area to the right, we simply give the T.INV function the value $1-\alpha/2$. - use
*df*=*n*– 1 degrees of freedom, and *s*= sample standard deviation.

**The format for the confidence interval is: **$(\bar x – E, \bar x + E)$

### Example 7.8

Suppose you do a study of acupuncture to determine how effective it is in relieving pain. You measure sensory rates for 15 subjects with the results given. Use the sample data to construct a 95% confidence interval for the mean sensory rate for the population (assumed normal) from which you took the data.

### Try It 7.8

You do a study of hypnotherapy to determine how effective it is in increasing the number of hours of sleep subjects get each night. You measure hours of sleep for 12 subjects with the following results. Construct a 95% confidence interval for the mean number of hours slept for the population (assumed normal) from which you took the data.

8.2; 9.1; 7.7; 8.6; 6.9; 11.2; 10.1; 9.9; 8.9; 9.2; 7.5; 10.5

### Example 7.9

The Human Toxome Project (HTP) is working to understand the scope of industrial pollution in the human body. Industrial chemicals may enter the body through pollution or as ingredients in consumer products. In October 2008, the scientists at HTP tested cord blood samples for 20 newborn infants in the United States. The cord blood of the “In utero/newborn” group was tested for 430 industrial compounds, pollutants, and other chemicals, including chemicals linked to brain and nervous system toxicity, immune system toxicity, and reproductive toxicity, and fertility problems. There are health concerns about the effects of some chemicals on the brain and nervous system. Table 7.3 below shows how many of the targeted chemicals were found in each infant’s cord blood.

79 | 145 | 147 | 160 | 116 | 100 | 159 | 151 | 156 | 126 |

137 | 83 | 156 | 94 | 121 | 144 | 123 | 114 | 139 | 99 |

Use this sample data to construct a 90% confidence interval for the mean number of targeted industrial chemicals to be found in an in infant’s blood.

### Try It 7.9

A random sample of statistics students were asked to estimate the total number of hours they spend watching television in an average week. The responses are recorded in Table 7.4 below. Use this sample data to construct a 98% confidence interval for the mean number of hours statistics students will spend watching television in one week.

0 | 3 | 1 | 20 | 9 |

5 | 10 | 1 | 10 | 4 |

14 | 2 | 4 | 4 | 5 |