**Determining the ideal sample size**

You want to set up a NPS survey, but you are not sure how many respondents you need? The number of respondents needed to get reliable results can be calculated based on three factors:

**How sure do you want to be in the results, i.e. your preferred** * confidence level*.

For example, when a decision can have fatal consequences (for instance while testing a new drug) you want a very high level of confidence in the results. When you are only exploring about rough designs or feelings without much impact of being wrong, a confidence level of 80% may be sufficient.

The * confidence level* refers to the percentage of all possible samples that can be expected to include the true population parameter. A 95% confidence level implies that 95% of the confidence intervals would include the true population parameter. The higher the confidence level, the less likely it is that the sample statistic is not reflecting the true population parameter.

**How precise your answers need to be, i.e. your preferred** * confidence interval*.

For example, a final poll on the day before a tight-running election would ask for a smaller confidence interval in order to predict the result with the most precision.

The * confidence interval *is the figure indicating the degree of uncertainty associated with a sample statistic. The confidence interval consists of a range of values with an acceptable margin of error around an unknown population parameter.

**How large is the group representing your potential customers, i.e. your population size (aka universe).**

For example, a sample of 50 respondents might be more relevant in a population of 75 than in a population of 100000. Of course in reality also cost considerations will be a decisive factor, as larger samples cost more to survey.

The** population size** is the magnitude of the group that represents your sample. This can be the number of people who have bought a specific product, or all potential clients for a service.