There are many ways the population and sample size can affect the survey you are conducting! Since you will have more people, there are many differences between a large sample size and a smaller size – let’s see the main effects that this large sample size can have on your survey as a whole!
The effects of population and sample size on your survey
One of the main aspects that can be affected in a survey if you change the population and sample size is the confidence level – this is the measure of trust or reliability that you have in your results. For example, if you find that you repeat the survey and that 95 out of 100 times the survey is replicated correctly, this provides you with a 95% confidence level. However, if you reduce your population and sample size for the survey to 10 people and you find that you still made a mistake twice, this brings you down to only an 80% confidence level – therefore, having a higher population and sample size is key to increasing the confidence level. By being able to estimate the level of uncertainty in your research, you can identify where you can potentially make mistakes and what may need to be fixed. You can see more about factors that affect your survey results here.
Margin of error
Furthermore, your population and sample size will influence the margin of error. If you are using the example that you have a 95% confidence level with your survey, but you are not sure that you will get a 95% confidence every single time you conduct the survey, this means that there is some margin of error on either side of this percentage. Basically, sometimes when you conduct the survey you may end up at 92%, and other times you may end up at 98% – in this case, it means that you have a 3% margin of error with a 95% confidence level.
Figure out differences
Another effect the population and sample size have on the survey that you are using is the ability, or the power, to see any differences and outliers in your survey. If you find that you are conducting a survey to determine a separation between two groups, this can help you calculate the observed effect size – this is the size between the two proportions, such as the difference between two groups of people.
The last aspect that population and sample size can influence in the survey is the effect size. The effect size is the size of the population and the sample can influence the significant level of the test – this means the level at which the survey stops being reasonable and helpful. If you find that the extraneous data does not really provide any answers to the questions that you are asking in your survey, then this is the significance level of the test – anything more or lower than this level is not helpful to the results.
By increasing the population and sample size to include more people, you may find that the results become more statistically significant and you can reduce the chance of the significance level happening at a lower rate.
As you can see, the size of the population and the sample size are two huge influencers in the way a survey is conducted. The number of people in the survey can influence the differences in the population, the effective size, margin of error, and confidence level of your survey.