Types of sampling design (with examples): How to choose the right method

While you’d certainly like to have information from every person in your target market when you’re conducting research, it just isn’t possible—but that doesn’t mean you can’t complete your research objectives.

When you need to gather data from your target market, you can select a representative sample to participate in the research. This sample is the foundation of your research, so you’ll need to select the best sampling design to obtain your sample.

Let’s take a closer look at sampling and sampling design, plus how they fit in with your market research needs.

Sampling design is the method you use to choose your sample. There are several types of sampling designs, and they all serve as roadmaps for the selection of your survey sample. The objective of sampling design is to ensure that your selected sample allows you to generalize your findings to the entire population you’re targeting.

Every sampling approach falls into one of two main categories: probability or non-probability sampling. Understanding which one fits your study is essential because it determines how confidently you can generalize your results.

When does SurveyMonkey Audience fit? If you don’t have a clean, complete sampling frame or need to move quickly, the SurveyMonkey Audience panel gives you targeted reach with screening and quotas to help you approximate the population you care about. If you already maintain a high-quality list, you can field to your own contacts within SurveyMonkey and apply probability-style designs where feasible.

These are the five most common probability sampling techniques. Each is based on random selection principles that make results generalizable to your target population.

What it is: With simple random sampling, every unit in the target population has an equal chance of being selected.

Best for: Works best for small to mid-size, well-defined lists when you want unbiased estimates.

Example: You have a 2,000-person company directory and randomly select 300 employees to measure satisfaction with a new benefits portal.

Common pitfalls: Results can be biased if your frame is incomplete (for example, missing certain members of your target population) or includes duplicate respondents.

What it is: With systematic sampling, you select every k-th record after a random start (for example, every 10th).

Best for: It’s a good fit for ordered lists where a touch of structure is fine and quick selection matters.

Example: You export all app users active in Q3, randomly start at position 7, and invite every 20th record until you reach your target.

Common pitfalls: Hidden patterns in your list—such as regional or alphabetical ordering—can bias results.

What it is: With stratified sampling, you divide your population into strata (for example, by region or gender) and sample from each group, often in proportion to its size.

Best for: It’s best when you need reliable subgroup comparisons and more precise overall estimates.

Example: You want to measure national brand awareness with dependable region-level reads, so you sample proportionately from the Northeast, Midwest, South, and West.

Common pitfalls: Problems arise when strata are defined incorrectly or when subgroups are over- or under-sampled without proper weighting.

What it is: With cluster sampling, you randomly select intact groups (clusters) and then survey everyone—or a subset—within those chosen clusters.

Best for: It’s useful when your population is geographically spread out or difficult to list in full.

Example: To evaluate IT support quality across a multi-office company, you randomly select five offices (clusters) and survey all employees in those locations.

Common pitfalls: Estimates can be less precise if clusters differ widely, and you need enough clusters to stabilize results.

What it is: Multistage sampling is a sampling method that selects samples in multiple steps: first choosing clusters, then sampling individuals within them. It combines elements of cluster and simple random sampling to make large-scale studies more manageable.

Best for: Ideal for large or geographically dispersed populations where listing every individual is impractical. Common in national or multi-regional surveys that require efficient, scalable fieldwork.

Example: A national research team first selects a random set of school districts (stage one), then randomly samples schools within those districts (stage two), and finally surveys teachers within each selected school.

Common pitfalls: Each stage introduces potential sampling error. Keep randomization consistent at every level, and document how selections were made to maintain transparency and replicability.

These are the five main types of non-probability sampling design in research, each using random selection to make results generalizable to your target population.

What it is: With convenience sampling, you recruit whoever is easiest to reach.

Best for: Quick pulse checks and early concept scoping.

Example: You intercept visitors on your website to test a new homepage layout.

Common pitfalls: Results often carry location or time bias, so they should not be generalized broadly.

What it is: With purposive sampling, researchers hand-pick participants based on traits relevant to the study.

Best for: Qualitative depth, niche B2B roles, or rare customer cohorts.

Example: You select 30 hospital administrators with electronic health record (EHR) purchasing power for an in-depth survey before conducting interviews.

Common pitfalls: Results may reflect the selection team’s biases, so it is important to document inclusion and exclusion rules.

What it is: Participants opt in after seeing an open call.

Best for: It is best for gathering ideas and themes from highly engaged users.

Example: You send a newsletter link asking power users for feedback on an advanced analytics feature.

Common pitfalls: Responses often skew toward strong opinions, whether highly positive or negative.

What it is: With snowball sampling, you recruit initial participants and ask them to refer others who meet your criteria.

Best for: It works well for reaching hidden or sensitive populations, such as security researchers or caregivers.

Example: You start with five cybersecurity leads, and each refers two peers to take part in a threat-intelligence survey.

Common pitfalls: Privacy and confidentiality require extra care, and network homogeneity can bias results.

What it is: With quota sampling, you set target counts for categories (for example, 50/50 gender or 30% Gen Z) and recruit until each quota is filled.

Best for: Approximating representativeness when probability sampling is not feasible.

Example: You conduct a retail study that requires 200 completions, split across age bands and income tiers.

Common pitfalls: Sampling bias can appear if category definitions exclude key groups (for example, non-binary genders) or if convenience drives recruitment within categories.

Choosing the right sampling method comes down to balancing rigor, speed, and practicality. Start by answering the three questions below, then match your scenario to the table to see which design and next step in SurveyMonkey fits best.

Use these three questions to land on a method fast:

Q1. Do you have a clean, comprehensive sampling frame with contact information for every member of the population?

Q2. Do you need generalizable estimates (with MOE) or exploratory signals?

Q3. What’s the best trade-off for your timeline and budget?

Use this matrix to weigh the strengths and limits of each sampling approach side by side. It highlights when to use each method, how they differ in data quality, speed, and cost, and what to watch for before you field in SurveyMonkey.

When you’re ready to begin, the process is straightforward. These five steps outline how to turn your sampling plan into a research-ready design.

1. Define your target population. Identify who you want to study and which subgroups matter most for your goals.

2. Choose your sampling frame. Decide where you’ll source respondents—your own list, a CRM, or SurveyMonkey Audience.

3. Select a sampling method. Use the decision table and comparison matrix above to choose the approach that best fits your study.

4. Determine your sample size. Use our sample size calculator to find the right balance of confidence level, margin of error, and feasibility.

5. Execute your plan. Launch your survey, track progress and quotas, and document any changes or adjustments as you field.

Each sampling design has its strengths and trade-offs. The key is choosing the method that fits your research goals, resources, and timeline. Review the designs we’ve covered, consider their benefits and limits, and decide which one gives you the insight you need most.

Then make execution simple with SurveyMonkey Audience. Set your audience traits, apply screeners or quotas, and field your study to verified respondents—all from one platform. Get real-time results, clean data, and analysis tools that help you move from design to decisions faster.

When you’re ready, get started free to launch your next study, or check out all of our market research solutions and find out how simple market research can be with the right tools.