### Survey Sampling

#### Introduction

#### Simple Random Samples

#### Stratified Samples

#### Cluster Samples

#### Sample Planning

#### Hypothesis Testing

#### Small Samples

#### Appendix

### Survey Sampling: Table of Contents

#### Introduction

- About This Tutorial
- Survey Sampling Overview
- Survey Sampling Methods
- Bias in Survey Sampling
- Survey Analysis

#### Simple Random Samples

#### Stratified Samples

#### Cluster Samples

#### Sample Planning

#### Hypothesis Testing

#### Small Samples

#### Appendix

# Sample Size: Simple Random Samples

This lesson describes how to find the smallest sample size that provides the desired precision, when the sampling method is simple random sampling.

## Factors That Influence Sample Size

The "right" sample size for a particular application depends on many factors, including the following:

- Cost considerations (e.g., maximum budget, desire to minimize cost).
- Administrative concerns (e.g., complexity of the design, research deadlines).
- Minimum acceptable level of precision.
- Confidence level.
- Variability within the population or subpopulation (e.g., stratum, cluster) of interest.
- Sampling method.

These factors interact in complex ways. Although a consideration of all the variations is beyond the scope of this tutorial, the remainder of this lesson covers a situation that commonly occurs with simple random samples: How to find the smallest sample size that provides the required precision.

The next lesson covers two situations that commonly occur with stratified random samples:

- How to get the most precision from a stratified sample, given a fixed sample size.
- How to get the most precision from a stratified sample, given a fixed budget.

For these, and for other situations not covered in the tutorial, consider using the Sample Size Calculator (described below).

## Sample Size Calculator

Stat Trek's Sample Size Calculator can help you find the right sample size quickly, easily, and accurately. You specify your main goal - maximize precision, minimize cost, stay within budget, etc. Based on your goal, the calculator prompts you for the necessary inputs and handles all computations automatically, allowing you to compare alternative designs and sample sizes. The calculator creates a summary report that lists key findings and describes analytical techniques. And the calculator is free. You can find the Sample Size Calculator in Stat Trek's main menu under the Stat Tools tab. Or you can tap the button below.

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## How to Choose Sample Size for a Simple Random Sample

Consider the following problem. You are conducting a survey to estimate a population mean or proportion. The sampling method is simple random sampling, without replacement. You want your survey to provide a specified level of precision.

To choose the right sample size for a simple random sample, you need to define the following inputs.

- Specify the desired
margin of error
*ME*. This is your measure of precision. - Specify
alpha.
- For a hypothesis test, alpha is the significance level.
- For an estimation problem, alpha is: 1 - Confidence level.

- Find the critical
standard score
*z*.- For an estimation problem or for a two-tailed hypothesis test, the critical standard score (z) is the value for which the cumulative probability is 1 - alpha/2.
- For a one-tailed hypothesis test, the critical standard score (z) is the value for which the cumulative probability is 1 - alpha.

- Unless the population size is very large relative to sample size (e.g., 20 times larger), you need to specify the size of the population (N).

You will also need to know the variance of the population, σ^{2}.
Given these inputs, the following formulas find the smallest sample
size that provides the desired level of precision.

Sample statistic |
Population size |
Sample size |
---|---|---|

Mean | Known | n = { z^{2} * σ^{2}
* [ N / (N - 1) ] }
/ { ME^{2}
+ [ z^{2} * σ^{2} / (N - 1) ] } |

Mean | Unknown | n = ( z^{2} * σ^{2} ) / ME^{2} |

Proportion | Known | n = [ ( z^{2} * p * q ) + ME^{2} ]
/ [ ME^{2} + z^{2} * p * q / N ] |

Proportion | Unknown | n = [ ( z^{2} * p * q ) + ME^{2} ]
/ ( ME^{2} ) |

This approach works when the sample size is relatively large (greater than or equal to 30). Use the first or third formulas when the population size is known. When the population size is large but unknown, use the second or fourth formulas.

For proportions, the sample size requirements vary, based on the value
of the proportion. If you are unsure of the right value to use, set
*p* equal to 0.5. This will produce a conservative sample
size estimate; that is, the sample size will produce *at least*
the precision called for and may produce better precision.

## Sample Problem

At the end of every school year, the state administers a reading test to a simple random sample drawn without replacement from a population of 100,000 third graders. Over the last five years, students who took the test correctly answered 75% of the test questions.

What sample size should you use to achieve a margin of error equal to plus or minus 4%, with a confidence level of 95%?

*Solution:* To solve this problem, we follow the steps outlined above.

- Specify the margin of error. This was given in the problem definition. The margin of error is plus or minus 4% or 0.04.
- Specify the confidence level. This was also given. The confidence level is 95% or 0.95.
- Compute alpha. Alpha is equal to one minus the confidence level. Thus, alpha = 1 - 0.95 = 0.05.
- Determine the critical standard score (z). Since this is an estimation problem, the critical standard score is the value for which the cumulative probability is 1 - alpha/2 = 1 - 0.05/2 = 0.975. To find that value, we use the Normal Calculator. Recall that the distribution of standard scores has a mean of 0 and a standard deviation of 1. Therefore, we plug the following entries into the normal calculator: Value = 0.975; Mean = 0; and Standard deviation = 1. The calulator tells us that the value of the standard score is 1.96.
- And finally, we assume that the population proportion
*p*is equal to its past value over the previous 5 years. That value is 0.75. Given these inputs, we can find the smallest sample size*n*that will provide the required margin of error.n = [ (z

^{2}* p * q ) + ME^{2}] / [ ME^{2}+ z^{2}* p * q / N ]n = [ (1.96)

^{2}* 0.75 * 0.25 + 0.0016] / [ 0.0016 + (1.96)^{2}* 0.75 * 0.25 / 100,000 ]n = (0.7203 + 0.0016) / ( 0.0016 + 0.0000072)

n = 449.2

Therefore, to achieve a margin of error of plus or minus 4 percent, we will need to survey 450 students, using simple random sampling.

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