# The Population Mean – Explanation & Examples

The definition of the population mean is:

“The population mean is the mean or average found in a population.”

In this topic, we will discuss the population mean from the following aspects:

• What is the population mean?
• How to find the population mean?
• The population mean formula.
• The role of the population mean.
• Practice questions.

## What is the population mean?

The population mean is the mean value of a numerical characteristic of the population. The population is the whole group of items we want to study. These items can be individuals, things, animals, plants, etc.

For example, the whole individuals living in the U.S., the whole chairs produced from a certain factory, the whole tigers living in rain forests in Indonesia, and the whole orange trees in Egypt.

These different populations’ numerical characteristics can be weights for individuals, leg lengths for chairs, tail lengths for tigers, and the heights for orange trees.

However, collecting information from the population may not be possible in many cases due to the great resources it needs.

For example, if we want to study the heights of American males. We can survey every American male and get his height. This is population data.

Alternatively, we can select 200 American males and measure their heights. This is sample data.

If we calculate the mean of the population data, its symbol is the Greek letter μ and pronounced “mu.”

## How to find the population mean?

We have two cases:

1. We have population data and so calculate the population mean from it.
2. We have sample data and use the sample mean to construct an interval that most likely contains the population mean.

### – Examples of population data

#### – Example 1

The following is the murder rate (per 100,000 population) for the 50 states of the U.S. in 1976. What is the mean of the murder rate?

We have information about all states of the U.S. so this is population data.

#### Note

This dataset may be considered as a sample or as a population.

It is a sample if we want to study the murder rate of the U.S. states in the 1970s, or it is a population if data is from 1970-1980 because this is a 1-year sample from these 10 years.

It is a population for the murder rate of U.S. states in 1976.

 state murder rate Alabama 15.1 Alaska 11.3 Arizona 7.8 Arkansas 10.1 California 10.3 Colorado 6.8 Connecticut 3.1 Delaware 6.2 Florida 10.7 Georgia 13.9 Hawaii 6.2 Idaho 5.3 Illinois 10.3 Indiana 7.1 Iowa 2.3 Kansas 4.5 Kentucky 10.6 Louisiana 13.2 Maine 2.7 Maryland 8.5 Massachusetts 3.3 Michigan 11.1 Minnesota 2.3 Mississippi 12.5 Missouri 9.3 Montana 5.0 Nebraska 2.9 Nevada 11.5 New Hampshire 3.3 New Jersey 5.2 New Mexico 9.7 New York 10.9 North Carolina 11.1 North Dakota 1.4 Ohio 7.4 Oklahoma 6.4 Oregon 4.2 Pennsylvania 6.1 Rhode Island 2.4 South Carolina 11.6 South Dakota 1.7 Tennessee 11.0 Texas 12.2 Utah 4.5 Vermont 5.5 Virginia 9.5 Washington 4.3 West Virginia 6.7 Wisconsin 3.0 Wyoming 6.9

1. Add up all of the numbers:

15.1+ 11.3+ 7.8+ 10.1+ 10.3+ 6.8+ 3.1+ 6.2+ 10.7+ 13.9+ 6.2+ 5.3+ 10.3+ 7.1+ 2.3+ 4.5+ 10.6+ 13.2+ 2.7+ 8.5+ 3.3+ 11.1+ 2.3+ 12.5+ 9.3+ 5.0+ 2.9+ 11.5+ 3.3+ 5.2+ 9.7+ 10.9+ 11.1+ 1.4+ 7.4+ 6.4+ 4.2+ 6.1+ 2.4+ 11.6+ 1.7+ 11.0+ 12.2+ 4.5+ 5.5+ 9.5+ 4.3+ 6.7+ 3.0+ 6.9 = 368.9.

2. Count the numbers of items in your population. In this population, there are 50 items or 50 states.

3. Divide the number you found in Step 1 by the number you found in Step 2.

The population mean = 368.9/50 = 7.378.

Note that the population mean has the same unit as the original data. So 7.378 is the mean murder rate per 100,000 population.

#### – Example 2

The following are the weights (in grams) for 71 chickens on a certain farm. What is the mean?

We have the weights of all chickens on the farm, so this is population data.

 chicken number weight 1 179 2 160 3 136 4 227 5 217 6 168 7 108 8 124 9 143 10 140 11 309 12 229 13 181 14 141 15 260 16 203 17 148 18 169 19 213 20 257 21 244 22 271 23 243 24 230 25 248 26 327 27 329 28 250 29 193 30 271 31 316 32 267 33 199 34 171 35 158 36 248 37 423 38 340 39 392 40 339 41 341 42 226 43 320 44 295 45 334 46 322 47 297 48 318 49 325 50 257 51 303 52 315 53 380 54 153 55 263 56 242 57 206 58 344 59 258 60 368 61 390 62 379 63 260 64 404 65 318 66 352 67 359 68 216 69 222 70 283 71 332

1. Add up all of the numbers:

179+ 160+ 136+ 227+ 217+ 168+ 108+ 124+ 143+ 140+ 309+ 229+ 181+ 141+ 260+ 203+ 148+ 169+ 213+ 257+ 244+ 271+ 243+ 230+ 248+ 327+ 329+ 250+ 193+ 271+ 316+ 267+ 199+ 171+ 158+ 248+ 423+ 340+ 392+ 339+ 341+ 226+ 320+ 295+ 334+ 322+ 297+ 318+ 325+ 257+ 303+ 315+ 380+ 153+ 263+ 242+ 206+ 344+ 258+ 368+ 390+ 379+ 260+ 404+ 318+ 352+ 359+ 216+ 222+ 283+ 332 = 18553.

2. Count the numbers of items in your population. In this population, there are 71 items or chickens.
3. Divide the number you found in Step 1 by the number you found in Step 2.

The population mean = 18553/71 = 261.3 grams.

#### – Example 3

The following is the trunk circumference (in mm) for 35 orange trees on a certain farm. What is the mean?

We have the trunk circumferences of all trees on the farm, so this is population data.

 tree number circumference 1 30 2 58 3 87 4 115 5 120 6 142 7 145 8 33 9 69 10 111 11 156 12 172 13 203 14 203 15 30 16 51 17 75 18 108 19 115 20 139 21 140 22 32 23 62 24 112 25 167 26 179 27 209 28 214 29 30 30 49 31 81 32 125 33 142 34 174 35 177

1. Add up all of the numbers:

30+ 58+ 87+ 115+ 120+ 142+ 145+ 33+ 69+ 111+ 156+ 172+ 203+ 203+ 30+ 51+ 75+ 108+ 115+ 139+ 140+ 32+ 62+ 112+ 167+ 179+ 209+ 214+ 30+ 49+ 81+ 125+ 142+ 174+ 177 = 4055.

2. Count the numbers of items in your population. In this population, there are 35 items or trees.

3. Divide the number you found in Step 1 by the number you found in Step 2.

The population mean = 4055/35 = 115.8571 mm.

### – Examples of sample data

For samples with a size greater than 30, the interval that, most likely, contains the population mean is calculated by:

¯x±1.96Xs/√n

Where:

¯x is the calculated sample mean.

s is the standard deviation of the sample. It is a measure of the data spread.

n is the sample size.

This interval (called 95% confidence interval) gives us a range of possible values for the unknown population mean from which the sample was taken.

#### – Example 4

The following is the age (in years) of 50 randomly selected individuals from a certain population. If you know that this sample’s standard deviation is 18.65, construct a 95% confidence interval for the true population mean.

89 61 74 85 46 60 41 18 37 30 44 37 51 53 74 38 56 48 52 62 33 56 38 30 43 32 74 27 49 53 40 27 42 60 88 22 59 43 69 75 28 47 35 62 65 31 22 31 26 83.

89+ 61+ 74+ 85+ 46+ 60+ 41+ 18+ 37+ 30+ 44+ 37+ 51+ 53+ 74+ 38+ 56+ 48+ 52+ 62+ 33+ 56+ 38+ 30+ 43+ 32+ 74+ 27+ 49+ 53+ 40+ 27+ 42+ 60+ 88+ 22+ 59+ 43+ 69+ 75+ 28+ 47+ 35+ 62+ 65+ 31+ 22+ 31+ 26+ 83 = 2446.

2. Count the numbers of items in your sample. In this sample, there are 50 items or persons.

3. Divide the number you found in Step 1 by the number you found in Step 2.

The sample mean = 2446/50 = 48.92 years.

4. The 95% confidence interval is:

¯x±1.96Xs/√n

¯x-1.96Xs/√n to ¯x+1.96Xs/√n

48.92-1.96X18.65/√50 to 48.92+1.96X18.65/√50 or 43.75 to 54.1.

It means that the true population mean age can be as small as 43.75 years and as large as 54.1 years.

Owing to the presence of the √n term in the formula for an interval calculation, the sample size affects the interval width. Larger sample sizes lead to smaller interval widths (or a more precise estimate of the population mean).

Suppose that you have a 100 sample size and you obtain the same sample mean and standard deviation; the 95% confidence interval will be:

48.92-1.96X18.65/√100 to 48.92+1.96X18.65/√100 or 45.26 to 52.6.

Suppose that you have a 500 sample size and you obtain the same sample mean and standard deviation; the 95% confidence interval will be:

48.92-1.96X18.65/√500 to 48.92+1.96X18.65/√500 or 47.29 to 50.55.

With increasing the sample size, you have more values about the true population mean.

We can see that in the following figure.

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