Sample size definition:
The sample size refers to the number of individual pieces of data collected in a survey. The sample size is important in determining the accuracy and reliability of a survey's findings.
In practice, the sample size used is determined based on the expense of data collection, and the need to have sufficient statistical power. When deciding the size of the sample, besides taking into account the size of the population, one must also think about how much error can be tolerated and how confident you want to be in your results.
The margin of error is the range of values above and below the sample statistic and is called the margin of error in a confidence interval. The most common values are 1%, 2.5% and 5%. This is the degree of variation that will be present in the results. We can use opinion polls as an example, which often have a margin of error of ±3%. This means that if a survey shows one party winning 58% of the votes, the results could in fact vary by 3% either way, so could actually be between 55% and 61%.
A confidence level refers to the percentage of all possible samples that can be expected to include the true population parameter. Suppose all possible samples were selected from the same population, then a 95% confidence level would imply that 95% of the confidence intervals would include the true population parameter.
When deciding on the sample size, you will need to consider both factors, margin of error and confidence interval, and then you can refer to the sample size table to find the sample size you should take.
Sample size table:
In this table, you will find the exact amount of responses you need based on the confidence level or margin of error.
As you can see from the table, the smaller you want the margin of error to be, the greater sample size you will need to take – the sample size increases as you go from 5% margin of error to 1% margin of error. As the confidence level increases and you want to be surer of the responses falling within the given range, you will also need a greater sample size.
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Let’s use this table now to calculate a sample size.
Sample size calculator:
If we want a 95% confidence interval and a 2.5% margin of error, the sample size calculator tells us that the size of the sample should be 377.
If you're looking for ways to increase your survey response rate: take a look at these 34 tips. You'll find a sample size calculator in tip number 8.
Be aware that there are other factors that should also be considered when choosing your sample size, such as the number of variables you will be analyzing (the greater number of variables, the larger sample size you will need) and whether you will be doing quantitative or qualitative research. Gathering qualitative data for too large a sample may end up being unworkable and unrealistic to analyze, and if you plan to use statistical analysis, a larger sample will allow for more sophisticated statistics to be calculated.