Best examples of judgmental sampling examples: practical insights

Judgmental sampling (also called purposive or expert sampling) is what happens when you say, “Let’s talk to these people, because they know the most or matter the most.” It shows up everywhere: in strategy decks, policy briefs, and UX reports, even when nobody uses the formal name.

Instead of starting with a definition, let’s jump into concrete, street-level examples of judgmental sampling and then pull out the patterns.

Marketing and customer research: picking “typical” and “extreme” users

Example of a retail brand choosing cities for a store concept test

A national clothing retailer wants to test a new store format before rolling it out. Rather than randomly picking locations, the insights team handpicks five cities:

• One high-income coastal metro
• One mid-sized Midwestern city
• One college town
• One tourist-heavy city
• One suburb with rapid population growth

They choose these locations based on analyst judgment: each city represents a strategic market segment, not a random slice of the country. Store performance and customer feedback in these “typical” and “high-priority” locations are then used to decide whether to expand.

This is a textbook case among the best examples of judgmental sampling examples: practical insights are drawn from markets the team expects to matter most. The upside is speed and relevance. The downside is obvious: if their intuition about which cities are “representative” is off, the rollout decision can be skewed.

Example of B2B interviews with “power users” only

A SaaS company building analytics software wants to improve its dashboard. The product manager doesn’t want broad coverage; she wants depth from users who push the tool to its limits. She asks sales and customer success to nominate:

• The five customers with the largest data volumes
• The three customers who file the most feature requests
• Two long-time customers who “know the product better than we do”

Interviews with these ten handpicked users drive the redesign. Again, this is judgmental sampling: examples include selecting users based on expertise, intensity of use, and perceived influence.

The team never claims the results are statistically representative. Instead, they’re looking for rich, high-signal feedback from people whose opinions they trust. That’s a smart use of judgmental sampling, as long as they don’t overgeneralize the findings to all users.

Public health and policy: targeting high-risk or hard-to-reach groups

Public health research is full of examples of judgmental sampling examples: practical insights gained when random sampling is either impossible or ethically messy.

Example of sampling for vaccine outreach in high-risk communities

During COVID-19, many local health departments needed quick insight into vaccine hesitancy in specific communities. Instead of trying to randomly sample everyone, they worked with:

• Faith leaders
• Community organizers
• Local clinic staff

These partners helped identify trusted individuals in neighborhoods with low vaccination rates. Health workers then conducted interviews or focus groups with those individuals to understand concerns and test messages.

This is judgmental sampling driven by local knowledge. The CDC’s guidance on community engagement during public health emergencies explicitly emphasizes working with trusted messengers and community partners, not just random samples (CDC). While the findings don’t represent the entire population, they provide targeted insights where they’re most needed.

Example of studying people who inject drugs for HIV prevention

Researchers studying HIV prevention strategies often focus on key populations such as people who inject drugs. Randomly sampling this group is extremely difficult due to stigma, legal risks, and mobility.

Instead, researchers may:

• Partner with harm-reduction programs or needle exchange sites
• Ask staff to nominate participants who are knowledgeable, articulate, or particularly connected in the community
• Conduct in-depth interviews or small surveys with this selected group

This is a clear example of judgmental sampling: examples include selecting participants based on access, risk profile, and willingness to share detailed information. The National Institutes of Health (NIH) and similar agencies often fund studies that rely on such purposive strategies for hard-to-reach populations (NIH).

Finance and risk analysis: focusing on edge cases

Example of a bank reviewing “borderline” loan applications

A bank rolling out a new automated credit scoring model doesn’t review a random set of past applications. Instead, risk analysts pull:

• Applications the model scored right at the approval cutoff
• Cases where the model’s decision conflicted with a human underwriter
• Accounts that later defaulted despite a strong initial score

Analysts then manually review these files to understand where the model might be over- or underestimating risk.

This is another of the best examples of judgmental sampling examples: practical insights are concentrated in edge cases, where mistakes are most costly. The sample is intentionally biased toward problematic decisions, because that’s where model weaknesses are most visible.

Example of stress-testing for specific economic scenarios

When regulators ask large banks to run stress tests, the scenarios are not random. They’re chosen by experts at the Federal Reserve and other agencies who imagine plausible but severe shocks: a sharp rise in unemployment, a housing price crash, or a spike in interest rates.

The portfolios and exposures examined in these tests are often selected judgmentally as well: segments that are historically vulnerable, or products that grew very quickly. While the Federal Reserve uses sophisticated models, human judgment still plays a major role in deciding what to scrutinize (Federal Reserve).

UX and product design: picking the “right” users, not all users

Example of usability testing with specific personas

A health app team wants to test a new medication reminder feature. The UX researcher doesn’t recruit users randomly from the entire app base. Instead, she screens for:

• Adults over 65 taking multiple daily medications
• Caregivers managing medications for a family member
• Patients recently discharged from a hospital

These participants are chosen because they match core personas and are judged to be the most affected by medication reminders. In other words, the researcher is using judgmental sampling.

Real examples of judgmental sampling examples: practical insights here include discovering that older adults prefer larger fonts and simpler flows, while caregivers want shared access and logging. None of this requires a random sample; it requires the right participants.

Example of early-stage AI product evaluation

In 2024–2025, many teams building AI tools (think coding assistants or legal research bots) are doing targeted evaluations. Instead of sampling random users, they:

• Recruit senior engineers, not interns, to test a coding assistant
• Ask experienced paralegals and attorneys to review legal search results
• Bring in domain experts to stress-test the model on tricky edge cases

These are modern examples of judgmental sampling: examples include handpicking evaluators who can reliably spot subtle errors. The findings won’t describe average user behavior, but they can quickly surface high-impact issues.

Media, social science, and qualitative research

Example of expert interviews for policy analysis

A think tank writing a report on the future of U.S. energy policy doesn’t survey random citizens. Instead, the research lead lines up interviews with:

• Former regulators
• Executives from major utilities
• Climate policy researchers at universities
• Leaders of environmental NGOs

These people are chosen because they are influential and informed, not because they are statistically representative. The goal is to map out plausible policy paths and political constraints, not estimate population averages.

This kind of expert interview study is one of the most common examples of judgmental sampling examples: practical insights flow from people whose decisions and opinions actually shape policy.

Example of purposive sampling in a qualitative study

A sociology professor studying gig workers might intentionally recruit:

• Drivers who work full-time for ride-hailing apps
• Part-time drivers in rural areas
• Drivers who have been deactivated
• Drivers who organize in online forums

This is not random; it’s designed to capture variation in experience. Many qualitative methods textbooks from universities like Harvard and others describe this kind of purposive sampling as standard practice in in-depth interview research (Harvard University).

Why people use judgmental sampling (and when it backfires)

Across all these real examples of judgmental sampling examples: practical insights come from a few recurring motivations:

• Speed: You can get insights fast without designing a full probability sample.
• Cost: Smaller, targeted samples are cheaper than large random surveys.
• Access: Some populations (e.g., undocumented workers, high-net-worth investors) are hard to reach randomly.
• Depth: You want rich, detailed data from people with specific knowledge or experiences.

But judgmental sampling has predictable risks:

• Bias: Your expert judgment might be wrong. You might systematically overlook important groups.
• Overconfidence: Teams often forget the sample is biased and start talking as if the results generalize to everyone.
• Blind spots: If you only talk to power users or experts, you may miss usability problems or needs of average users.

A practical rule of thumb: judgmental sampling is most defensible when your goal is exploration, hypothesis generation, or design, not precise population estimates.

How to use judgmental sampling more intelligently

If you’re going to use it—and in real work, you probably are—there are ways to make judgmental sampling more disciplined.

Be explicit about your selection criteria

In many of the best examples of judgmental sampling examples: practical insights came from teams that were honest about why they picked certain participants:

• “We selected cities that represent our three largest revenue segments.”
• “We recruited heavy users because we needed feedback from people who use advanced features daily.”
• “We interviewed community leaders because they influence others’ health decisions.”

Writing down your criteria forces you to confront your assumptions and makes it easier for others to critique or replicate your work.

Combine judgmental sampling with other methods

Some of the strongest real examples of judgmental sampling examples: practical insights come from hybrid designs:

• A UX team starts with judgmental sampling of power users to identify key issues, then runs a larger survey with a more random sample to see how common those issues are.
• A public health team interviews community leaders judgmentally, then uses a structured questionnaire in a broader, more systematic sample to measure prevalence.
• A bank uses judgmental sampling to review edge cases in a credit model, then validates changes on a large, randomly selected historical dataset.

Judgmental sampling is often best as a first pass or a deep dive, not the only method.

Always label your findings accurately

If your sample is judgmental, say so. Don’t hide it behind vague language.

Instead of:
“Users prefer design A over design B.”

Say:
“In interviews with ten judgmentally selected power users, most preferred design A over design B. Further testing with a broader sample is needed.”

This kind of transparency is standard in serious research and makes your work more credible.

FAQ: common questions about examples of judgmental sampling

What is an example of judgmental sampling in everyday business?

One everyday example of judgmental sampling is a sales director asking regional managers to nominate their “most representative” customers for feedback calls. Those customers are not randomly selected; they’re chosen based on the managers’ judgment of who best reflects typical needs or who has the most strategic value.

Are examples of judgmental sampling always biased?

They are always non-random, which means you cannot calculate sampling error in the usual statistical sense. That doesn’t automatically make them useless or misleading. When used transparently for exploratory work, design, or understanding specialized groups, they can be very informative. Problems arise when teams pretend that judgmentally selected samples represent the whole population.

Can judgmental sampling be used in academic research?

Yes. Many qualitative and mixed-methods studies in sociology, education, public health, and political science rely on purposive or judgmental sampling. Real examples include studies of marginalized groups, expert interviews on policy, or case studies of specific organizations. The key is to be explicit about the method and honest about what the findings can—and cannot—say.

How are examples of judgmental sampling different from convenience sampling?

In convenience sampling, you talk to whoever is easiest to reach: people walking through a mall, students in your class, followers on your social media account. In judgmental sampling, you actively choose participants based on their relevance, expertise, or characteristics. Both are non-probability methods, but judgmental sampling involves more deliberate selection.

When should I avoid using judgmental sampling?

Avoid it when you need population estimates that will be used for high-stakes decisions: forecasting national election results, estimating disease prevalence, or measuring unemployment rates. In those cases, you want probability-based methods, like those used by the U.S. Census Bureau or CDC surveys. Judgmental sampling examples: practical insights are better suited to design, discovery, and understanding mechanisms—not to standing in for the entire population.