Sampling Demystified: Probability vs. Non-Probability Sampling

Sampling can be a confusing concept for managers carrying out survey research projects. By knowing some basic information about survey sampling designs and how they differ, you can understand the advantages and disadvantages of various approaches.

The two main methods used in survey research are probability sampling and non-probability sampling. The big difference is that in probability sampling all persons have a chance of being selected, and results are more likely to accurately reflect the entire population. While it would always be nice to have a probability-based sample, other factors need to be considered (availability, cost, time, what you want to say about results).

Some additional characteristics of the two methods are listed below.

Probability Sampling

• You have a complete sampling frame. You have contact information for the entire population.
• You can select a random sample from your population. Since all persons (or “units”) have an equal chance of being selected for your survey, you can randomly select participants without missing entire portions of your audience.
• You can generalise your results from a random sample. With this data collection method and a decent response rate, you can extrapolate your results to the entire population.
• Can be more expensive and time-consuming than convenience or purposive sampling.

Non-probability Sampling

• Used when there isn’t an exhaustive population list available. Some units are unable to be selected, therefore you have no way of knowing the size and effect of sampling error (missed persons, unequal representation, etc.).
• Not random.
• Can be effective when trying to generate ideas and getting feedback, but you cannot generalise your results to an entire population with a high level of confidence. Quota samples (males and females, etc.) are an example.
• More convenient and less costly, but doesn’t hold up to expectations of probability theory.