What is the importance of what a sample size is and how a sample size should be used in a procedure?

Whatever market research you’re conducting or survey you’re running in support of it to better understand your consumers, customers or the performance of your products, it’s usually impossible, or at least impractical to collect data from all the people or items that you’re interested in.

Subsequently, researchers will take a sample (or subset) of their population of interest instead and look to see what insights they can learn from that sample, in order to make some presumptions about the behaviours and opinions of their wider and larger target population group.

Why is sample size important in research?

Even before we explore the significance for surveys, the value of deciding upon sample size is quickly brought into sharp focus when we consider the well documented importance of sample size in quantitative research and qualitative research.

In quantitative research, the ability to draw conclusions with a reasonable amount of confidence relies on having an accurate sample size calculation, as without this it can lead to results being missed, biased or just plain incorrect. It can also result in rendering a study unethical, unpublishable, or both. And while qualitative research projects don’t usually require as large a sample size as quantitative research, they still need to be large enough to be able uncover the variety of opinion necessary to address their questions and offer findings of valuable enough interest to their area of study.

Benefits of a large sample size

In contrast to its importance in research, when it comes to the importance of sample size in online surveys what we’re referring to is the number of completed responses a specific survey generates.

The general consensus is that the larger your sample size, or higher your volume of survey responses as it’s otherwise known, the more reliable your results will be.

However, it’s not quite as simple as that, as the margins between obtaining or falling short of a statistically significant sample size can be extremely narrow.

For example, if you were running a market research survey in order to discover more about the purchasing habits of sports enthusiasts and their choice of running shoes, and your sample size contained too few responses, it could result in data that is unrepresentative of your target population and inaccurate results that are not sufficient to support your decision making.

In contrast, if your sample size yielded too many responses, the analysis of this data could put a huge strain on your available time and budgetary resources. So, much so that any benefits that your research produced could end up being outweighed by your costs.

Subsequently, before creating a survey, it makes sense that in addition to ensuring you generate the most accurate results possible, you calculate a survey response rate that will also maximise the time and budget available to you.

Sample size calculation

When it comes to how to calculate sample size there are a number of ways in which you could potentially do this including using a sample size from a similar study, doing it manually or using a calculator.

Use a similar study

There are lots studies around, so the chances of finding one similar to your own is relatively high, especially if you use a search tool such as the best academic research databases.

You can use their sample size to create your own. However, it is important to be aware that if the researchers didn’t do their calculations correctly, it could impact the credibility of your study.

Carry out a census

If your target population size is relatively small, you might consider taking a census instead of using a sample. Keeping track of a team of 100 people is relatively easy, so you could conduct a census and create a statistically robust study.

However, if your study involved thousands of people, then it would be almost impossible to survey everyone, let alone make sure each person answered your questions.

Work it out manually

If you have strong maths skills, then you might want to try calculating it manually. There are a number of formulas available to do this including Cochran and Slovin’s.

However, calculating your sample size is difficult, and even statisticians can struggle with these manual calculations. So, for accuracy, we would recommend you using a calculator.

Use a sample size calculator

The sample size calculator offers one of the simplest ways to define your sample size. Fortunately, we’ve created a sample size calculator to help you. And because it allows you to input the data for your population size, confidence level and margin of error, you will be able to calculate sample size with the required accuracy that you need in just a few short simple steps. Try it now, to see how easy it is to use.

Sample size is a frequently-used term in statistics and market research, and one that inevitably comes up whenever you’re surveying a large population of respondents. It relates to the way research is conducted on large populations.

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So what is sampling, and why does sample size matter?

When you survey a large population of respondents, you’re interested in the entire group, but it’s not realistically possible to get answers or results from absolutely everyone. So you take a random sample of individuals which represents the population as a whole.

The size of the sample is very important for getting accurate, statistically significant results and running your study successfully.

• If your sample is too small, you may include a disproportionate number of individuals which are outliers and anomalies. These skew the results and you don’t get a fair picture of the whole population.
• If the sample is too big, the whole study becomes complex, expensive and time-consuming to run, and although the results are more accurate, the benefits don’t outweigh the costs.

If you’ve already worked out your variables you can get to the right sample size quickly with the online sample size calculator below:

Confidence Level:
90%95%99%

Margin of Error:
1%2%3%4%5%6%7%8%9%10%

If you want to start from scratch in determining the right sample size for your market research, let us walk you through the steps.

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Learn how to determine sample size

To choose the correct sample size, you need to consider a few different factors that affect your research, and gain a basic understanding of the statistics involved. You’ll then be able to use a sample size formula to bring everything together and sample confidently, knowing that there is a high probability that your survey is statistically accurate.

The steps that follow are suitable for finding a sample size for continuous data – i.e. data that is counted numerically. It doesn’t apply to categorical data – i.e. put into categories like green, blue, male, female etc.

Download your sample size guide now, including Z-score table.

Stage 1: Consider your sample size variables

Before you can calculate a sample size, you need to determine a few things about the target population and the level of accuracy you need:

1. Population size

How many people are you talking about in total? To find this out, you need to be clear about who does and doesn’t fit into your group. For example, if you want to know about dog owners, you’ll include everyone who has at some point owned at least one dog. (You may include or exclude those who owned a dog in the past, depending on your research goals.) Don’t worry if you’re unable to calculate the exact number. It’s common to have an unknown number or an estimated range.

2. Margin of error (confidence interval)

Errors are inevitable – the question is how much error you’ll allow. The margin of error, AKA confidence interval, is expressed in terms of mean numbers. You can set how much difference you’ll allow between the mean number of your sample and the mean number of your population. If you’ve ever seen a political poll on the news, you’ve seen a confidence interval and how it’s expressed. It will look something like this: “68% of voters said yes to Proposition Z, with a margin of error of +/- 5%.”

3. Confidence level

This is a separate step to the similarly-named confidence interval in step 2. It deals with how confident you want to be that the actual mean falls within your margin of error. The most common confidence intervals are 90% confident, 95% confident, and 99% confident.

4. Standard deviation

This step asks you to estimate how much the responses you receive will vary from each other and from the mean number. A low standard deviation means that all the values will be clustered around the mean number, whereas a high standard deviation means they are spread out across a much wider range with very small and very large outlying figures. Since you haven’t yet run your survey, a safe choice is a standard deviation of .5 which will help make sure your sample size is large enough.

Stage 2: Calculate sample size

Now that you’ve got answers for steps 1 – 4, you’re ready to calculate the sample size you need. This can be done using the online sample size calculator above or with paper and pencil.

5. Find your Z-score

Next, you need to turn your confidence level into a Z-score. Here are the Z-scores for the most common confidence levels:

• 90% – Z Score = 1.645
• 95% – Z Score = 1.96
• 99% – Z Score = 2.576

If you chose a different confidence level, use our Z-score table to find your score.

6. Use the sample size formula

Plug in your Z-score, standard of deviation, and confidence interval into the sample size calculator or use this sample size formula to work it out yourself:

This equation is for an unknown population size or a very large population size. If your population is smaller and known, just use the sample size calculator above, or find it here.

What does that look like in practice?

Here’s a worked example, assuming you chose a 95% confidence level, .5 standard deviation, and a margin of error (confidence interval) of +/- 5%.

((1.96)2 x .5(.5)) / (.05)2

(3.8416 x .25) / .0025

.9604 / .0025

384.16

385 respondents are needed

Voila! You’ve just determined your sample size.

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Troubleshooting your sample size results

If the sample size is too big to manage, you can adjust the results by either

• decreasing your confidence level
• increasing your margin of error

This will increase the chance for error in your sampling, but it can greatly decrease the number of responses you need.