How can you select a sample if the population is unknown?

1 Non-probability sampling

One approach is to use non-probability sampling, which means that the sample is not randomly selected and does not reflect the population proportionally. Non-probability sampling can be useful when the population is hard to identify, reach, or measure, or when the sampling criteria are based on convenience, availability, or judgment. However, non-probability sampling also has some drawbacks, such as potential bias, limited generalizability, and difficulty in estimating sampling error.

2 Probability sampling

Another approach is to use probability sampling, which means that the sample is randomly selected and represents the population with a known margin of error. Probability sampling can provide more accurate and reliable results, but it also requires more information and resources. To use probability sampling when the population is unknown, you need to make some assumptions or use some techniques to estimate the population parameters, such as size, variance, or distribution.

3 Sampling frames

A sampling frame is a list or a map of all the units or elements in the population that can be sampled. Ideally, the sampling frame should match the population exactly, but in reality, this is often not possible. When the population is unknown, you may face some challenges in creating or obtaining a sampling frame, such as incomplete or outdated records, missing or duplicated entries, or inaccessible or hidden units. To overcome these challenges, you may need to use multiple sources, update or verify the data, or apply some corrections or adjustments.

5 Sampling size

The sample size is the number of units or elements that are included in the sample. The sample size affects the precision and validity of the statistical inference that can be made from the sample to the population. To determine the sample size, you need to consider some factors, such as the level of confidence, the margin of error, the population variability, and the design effect. When the population is unknown, you may need to use some formulas or methods to estimate these factors, such as pilot studies, previous studies, or expert opinions.

6 Sampling quality

The quality of the sample refers to how well the sample reflects the population and how free it is from errors or biases. The quality of the sample depends on the sampling method, the sampling frame, the sampling technique, and the sampling size, as well as the data collection and analysis processes. To evaluate the sampling quality, you need to assess some indicators, such as the response rate, the nonresponse bias, the sampling error, and the representativeness of the sample.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?