Sample Size Definition
In statistics, a sample refers to the observations drawn from a population. Sample size is used in market research and defines the number of subjects that should be included within a sample. Having the right sample size is crucial in finding a statistically significant result. The larger the sample size, the more reliable the results; however, larger sample size means more time and money. So, how do you determine the right sample size for your market research?
Learn How to Determine Sample Size
Consequential research requires an understanding of the statistics that drive the range of sample size decisions you need to make. A simple equation will help you put the migraine pills away and sample confidently knowing that there is a high probability that your survey is statistically accurate with the correct sample size.
Sample Size Variables Based on Target Population
Before you can calculate a sample size, you need to determine a few things about the target population and the sample you need:
- Population Size — How many total people fit your demographic? For instance, if you want to know about mothers living in the US, your population size would be the total number of mothers living in the US. Not all populations sizes need to be this large. Even if your population size is small, just know who fits into your demographics. Don’t worry if you are unsure about this exact number. It is common for the population to be unknown or approximated between two educated guesses.
- Margin of Error (Confidence Interval) — No sample will be perfect, so you must decide how much error to allow. The confidence interval determines how much higher or lower than the population mean you are willing to let your sample mean fall. If you’ve ever seen a political poll on the news, you’ve seen a confidence interval. For example, it will look something like this: “68% of voters said yes to Proposition Z, with a margin of error of +/- 5%.”
- Confidence Level — How confident do you want to be that the actual mean falls within your confidence interval? The most common confidence intervals are 90% confident, 95% confident, and 99% confident.
- Standard of Deviation — How much variance do you expect in your responses? Since we haven’t actually administered our survey yet, the safe decision is to use .5 – this is the most forgiving number and ensures that your sample will be large enough.
Calculating Sample Size
Okay, now that we have these values defined, we can calculate our needed sample size. This can be done using an online sample size calculator or with paper and pencil.
Your confidence level corresponds to a Z-score. This is a constant value needed for this equation. 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 choose a different confidence level, use this Z-score table* to find your score.
Next, plug in your Z-score, Standard of Deviation, and confidence interval into the sample size calculator or into this equation:**
Sample Size Formula
Necessary Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2
Here is an example of how the math works 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
385 respondents are needed
You’ve just determined your sample size.
If you find your sample size is too large to handle, try slightly decreasing your confidence level or 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.
*Credit to SJSU