# How to Choose Between Simple, Systematic, and Stratified Sampling

Choosing the proper sampling method for your spreadsheet data is crucial for accurate results. This article explores the strengths and weaknesses of three common techniques, simple random, systematic, and stratified sampling, to help you choose the best tool.

## Simple

Simple random sampling shines when the population meets these conditions:

• Uniform population: The key advantage of simple random sampling is that every member of the population has an equal chance of being selected. This works best if you believe the population itself is homogenous, with no underlying subgroups that might be systematically under-represented.
• Feasible random selection: Simple random sampling requires a complete list of the population and a way to randomly select individuals from that list. If creating or accessing such a list is difficult or expensive, it might not be practical.
• Limited resources: While conceptually straightforward, implementing simple random sampling for very large populations can be time-consuming. If resources are limited, another method might be more efficient.

Here are some scenarios where simple random sampling might be a good choice:

• Selecting a lottery winner: In a perfect lottery, every ticket has an equal probability of winning. Simple random sampling ensures fairness by giving each ticket an equal chance.
• Small, well-defined group: If you’re surveying a small class of students or a department within a company, a simple random sample is likely to be representative as long as the group itself is homogenous.
• Pilot study: For a pilot study with a limited sample size, simple random sampling can be a quick way to get a preliminary sense of the population without needing to overthink subgroups or hidden patterns.

In conclusion, simple random sampling is a great choice when you have a uniform population, can easily select members randomly, and don’t have extensive resources for a more complex sampling method. It ensures unbiased selection and can be a solid foundation for drawing inferences about the larger population.

## Systematic

There are situations where systematic sampling might be preferable to simple random sampling. Here’s when systematic sampling can be a good choice:

• Ease of implementation: Systematic sampling is often simpler than simple random sampling. With systematic, you pick a random starting point and select every nth element on a list. This can be quicker, especially for large populations.
• Ordered population: If the population has a natural order and you’re confident it’s random within itself, systematic sampling can be a good choice. For example, if you’re surveying every 10th house on a long street, assuming the houses weren’t built in a specific order by family type, you can get a good representation.

However, there’s a potential drawback to consider:

• Hidden periodicity: If there’s a hidden pattern in the order of the population that matches your sampling interval (nth element), it can lead to a biased sample. For instance, if you’re surveying customers at several stores every 7th day that have a weekly cleaning cycle, you might miss certain demographics who tend to shop on specific days.

## Stratified

You should use stratified sampling instead of simple random when dealing with a population you know is not uniform. Here’s a breakdown of the key reasons to choose stratified:

• Heterogeneous population: If the population has subgroups (strata) with different characteristics, a simple random sample might not accurately represent each group. Stratified sampling proportionately represents each subgroup is in the final sample.
• More precise estimates: By ensuring subgroups are included, stratified sampling often leads to more accurate estimates of the population mean or total. This is because the variation within each subgroup is typically smaller than the variation in the entire population.
• Targeted analysis: Stratification allows you to analyze results for each subgroup independently. This can be useful for understanding how different groups differ. You can skip certain groups completely.

For instance, imagine you want to survey college students about their preferred learning style. A simple random sample might under-represent freshmen or grad students. With stratified sampling, you can divide the population into strata (freshmen, sophomores, etc.) to ensure you fairly represent each group in the final sample. This way, you get a more accurate picture of learning style preferences across different year levels.

⚠️ Our Random Sampler add-on for Google Sheets can perform any of these methods with no code.

## Conclusion

Here’s a simple rule of thumb:

• Choose simple random if you have a uniform population and want maximum randomness.
• Choose systematic if you need an easier method and are confident there’s no hidden periodicity in the population order.
• Choose stratified if you know the population has subgroups and you want to consider those groups separately.