To make an informed business decision, you need to start by collecting data.
For example, if you want to understand your customers’ wants and needs or determine whether your brand messaging resonates with your target audience, you might use surveys, customer interviews, focus groups, or field trials. Similarly, if you want to assess the prevalence of defects or flaws in your products, you might inspect your inventory.
Unfortunately, you'll rarely be able to survey every customer or inspect every product your organization produces. Instead, organizations typically rely on a representative sample and use statistical analysis to draw conclusions about the broader population. Understanding the difference between a population and a sample is a core business analytics skill. Without understanding how populations and samples are defined, selected, and related, your statistical analysis—and the decisions based on it—may be flawed.
This guide explains the difference between a population and a sample, explores the risks of sampling bias, and offers guidance for choosing an appropriate sample size.
What Are Population and Sample in Statistics
In statistics, a population is the complete group of people, events, products, transactions, or other units you're trying to study. As noted in the Harvard Business School Online course Business Analytics, part of the credential, Core Business Essentials, this complete group may also be called the population of interest.
A sample is a smaller subset selected from the population of interest. Because it's more manageable to analyze than the full population, analysts use a sample to conclude the broader population.
Random sampling is often used to reduce the risk of bias, but it isn't the only sampling method. Other common approaches include:
Stratified sampling, in which the population is divided into subgroups—such as age or income level—before sampling, to ensure that the sample includes all relevant subgroups
Systematic sampling, in which the sample's subjects are chosen using an interval—for example, every 5th transaction or every 10th product
Convenience sampling, in which the most readily available subjects are selected rather than chosen randomly
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When Should You Sample: A Practical Framework
Selecting a sample from your broader population of interest often makes analysis easier, faster, and more cost-effective than studying the entire population. However, sampling isn't always the right approach. In fact, in some situations, sampling could be detrimental to your organization’s goals.
The online course Business Analytics presents a framework for deciding whether sampling is appropriate for a given situation, which asks you to evaluate:
The size and structure of the population
The importance of individual cases
The ability to revisit any decisions informed by the analysis
The consequences of acting on incomplete information
When Is Sampling Appropriate?
Under this framework, sampling is appropriate in situations where the population of interest is large, and where no single case has the potential to skew or determine the outcome of the analysis. In these situations, a representative sample can allow analysts to identify patterns that may apply to the broader population.
Sampling is also appropriate when decisions can be reversed relatively easily or when the consequences of acting on incomplete information are manageable.
When Is Sampling Inappropriate?
Sampling isn't appropriate when the population of interest is small enough that individual cases can significantly skew the results of your analysis, or when it's manageable to study the entire population.
Sampling also isn't appropriate if the decisions informed by your analysis would be difficult or impossible to reverse—or if the cost of getting it wrong is extremely high. In these situations, organizations often need more comprehensive evidence, which may require analyzing the full population rather than a sample.
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Controlling for Bias
Although sampling offers many benefits, it isn’t without risks. One of the biggest risks is that you might inadvertently introduce statistical bias into your analysis when constructing your sample.
In statistics, bias is a systematic error that causes the results of your analysis to become skewed. As noted in Business Analytics, it’s often believed that bias is caused by errors during analysis, but it's often introduced during the sampling process. For this reason, bias is one of the primary reasons that sampling can fail, even when results appear to be credible.
To better control for bias as you construct your sample, you should consider these three common ways that bias tends to enter before analysis, as presented in Business Analytics:
Who is easiest to observe? The people who are more likely to submit complaints, respond to a request for an interview or survey, or show up for a focus group may not always be representative of the population of interest.
Who opts in versus staying silent? When a sample is constructed via voluntary channels, such as feedback forms, user reviews, or internal forums, it can amplify extreme experiences and stronger opinions because people with neutral experiences or opinions often don’t contribute.
How might your data source be limited? If the dataset used to generate your sample is limited—for example, it only contains recent transactions or only incidents that required further investigation—results of your analysis can be skewed if you treat the sample as representative of the full picture.
While you’re creating a sample and reviewing data, ask yourself: Who is represented in the dataset, and who might be systematically missing or omitted?
Getting Your Sample Size Right
Choose a sample size that's appropriate for both your research question and the population you're studying.
As noted in Business Analytics, a certain degree of variation within a population is normal, but the effect of this variation on your analysis will be very different when the sample is too small. That’s because, in smaller samples, outliers and unique experiences are given more weight than if they were a part of larger samples. For this reason, larger samples tend to produce more stable averages during analysis than smaller samples, leading to more accurate results.
Imagine you're evaluating your sales team's performance using customer surveys. If your sample includes only 10 surveys and two are extremely negative, those responses can significantly influence the average. If your sample includes 200 surveys, those same responses are still reflected—but they'll have far less influence on the overall results.
Still, it’s important to recognize that a larger sample size cannot eliminate bias if the sampling itself is flawed.
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The Importance of Analytical Skills
Sampling can be a powerful analytical tool, allowing you to complete analysis faster and more cost-effectively than trying to study your entire population of interest. Unfortunately, inappropriate sampling can often cause more harm than good. Relying on a sample that’s too small or that’s not representative of your population could mean that you’re drawing conclusions—and making decisions—based on incomplete data.
Knowing how to select a sample and how large it should be requires a clear understanding of how data, probability, and analytics work. To develop this understanding, consider enrolling in an online course like Business Analytics, which is specifically designed to prepare you to interpret data, test business ideas, and apply predictive insights to your decision-making process.
Are you ready to develop your understanding of business data to better lead your team and organization? Explore Business Analytics—part of our online credential Core Business Essentials—or download our free brochure today.