describe the steps in the process of sampling

February 15, 2026

Sampling in research usually follows a simple, ordered set of steps: define who you want to study, decide where you’ll find them, choose how you’ll pick them, decide how many you need, and then actually select the sample and collect data.

Quick Scoop: What is “sampling” in research?

Sampling is the process of selecting a smaller group (sample) from a larger group (population) so you can study the smaller group and draw conclusions about the larger one. This is used in opinion polls, market research, academic studies, and even business analytics when studying every single member of a population would be too expensive or impossible.

Think of sampling like tasting a spoonful of soup from a big pot: if that spoonful is chosen well and mixed properly, you can judge the whole pot from just a small portion.

Step 1: Define the target population

Before anything else, you must clearly describe the entire group you want your findings to apply to (the target population).

Key points:

Answer: “About whom do I want to conclude?” (e.g., “all undergraduate students in my university this year”).

Specify: time period, geography, and key characteristics (age range, role, etc.).

This step prevents you from accidentally collecting data from the wrong people and then drawing misleading conclusions.

Step 2: Specify the sampling frame

Once you know the population, you need a sampling frame —a concrete list or source from which you will actually draw your sample.

Typical examples:

Voters: electoral register or list of registered voters.

Customers: company customer database or purchase records.

Students: official enrollment list from the registrar.

A good sampling frame:

Includes every member of the target population, as far as possible.
Excludes people who do not belong to that population (to avoid bias).

If there are mismatches (missing members, outdated records, duplicates), this can create coverage error and bias your results, so this step often requires careful checking and cleaning.

Step 3: Specify the sampling unit

A sampling unit is the basic element you will select at each draw: it might be a person, a household, a school, or even a whole neighborhood.

Examples:

Household survey → sampling unit = household.
School performance study → sampling unit = school or class.
Consumer survey → sampling unit = individual customer.

Clarifying the sampling unit is crucial, especially when you plan to use cluster or multistage sampling where you might first sample groups (like schools) and only later sample individuals within them.

Step 4: Choose the sampling method or design

Next you choose how you will select units from the frame—this is your sampling method or design.

A. Probability sampling (random-based)

Every unit has a known, non-zero chance of selection. Common types:

Simple random sampling: each element has an equal chance; often implemented with random numbers.

Systematic sampling: select every kkk-th element (e.g., every 10th name), with a random start between 1 and kkk.

Stratified sampling: split the population into strata (e.g., age groups) and randomly sample within each to ensure representation.

Cluster sampling: divide into clusters (e.g., schools or city blocks) that each resemble the population; randomly select some clusters, then all or some members within them.

Probability sampling is preferred when you want strong, statistically defensible generalizations and error estimates.

B. Non-probability sampling (non-random)

Not every member has a known chance of selection; used when probability sampling is impossible or too costly. Examples include convenience sampling, quota sampling, and purposive sampling.

The trade-off: easier and cheaper, but more vulnerable to bias and weaker for formal statistical inference.

Step 5: Determine the sample size

Now you decide how many units you need to include in your sample.

Factors influencing sample size:

Desired precision and confidence (e.g., narrow margin of error in a poll).

Population variability (more variation → typically need a larger sample).

Practical constraints: time, budget, and data-collection capacity.

In many studies, researchers use statistical formulas or online calculators that take into account confidence level, acceptable error, and an estimate of the population proportion or variance. In applied settings like business, feasibility often forces a compromise between ideal theoretical size and what is realistically achievable.

Step 6: Develop the detailed sampling plan

Before actually drawing the sample, you translate everything into a clear, step-by-step sampling plan.

A sampling plan usually specifies:

Exactly how the frame will be accessed and cleaned (e.g., removing duplicates, ineligible cases).

The precise procedure for random selection or other method (e.g., which random generator, how to handle refusals or non-response).

Rules for replacements or callbacks when selected units cannot be reached.

This plan ensures that, when fieldwork starts, everyone on the research team follows the same protocol, reducing human error and bias.

Step 7: Select the sample and execute the plan

Finally, you draw the sample according to the plan and carry out data collection.

Typical actions in this step:

Use the chosen method to pick specific units from the frame (e.g., selected names in a list, chosen classrooms, chosen city blocks).

Contact those units and collect data using surveys, interviews, observations, or measurements.

Track non-responses, refusals, and dropouts to understand and, where possible, adjust for non-response bias.

Good practice also includes keeping data in time order, recording contextual notes, and documenting any deviations from the original plan.

Putting it all together (mini example)

Imagine you want to “describe the steps in the process of sampling” for a student satisfaction survey at a college:

Define population: all full-time undergraduate students enrolled in 2025–26 at the college.

Sampling frame: official enrollment list from the registrar’s office.

Sampling unit: individual student.

Sampling method: stratified random sampling by department to ensure each department is represented.

Sample size: use a formula to decide that 400 students are enough for the desired error margin and confidence level, given time and budget.

Plan: spell out exactly how to randomize within each department, how many reminders to send, and what to do if students do not respond.

Execute: draw the random list of students, email them the survey, track responses, and record non-response patterns.

HTML table: Main steps in the sampling process

html

<table>
  <thead>
    <tr>
      <th>Step</th>
      <th>Name</th>
      <th>Core Question</th>
      <th>Main Output</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Define target population</td>
      <td>Who do we want to generalize to?</td>
      <td>Clear description of the population of interest.[web:1][web:5][web:9]</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Specify sampling frame</td>
      <td>Where will we list and find them?</td>
      <td>Operational list or database of population members.[web:1][web:2][web:6][web:9]</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Specify sampling unit</td>
      <td>What exactly are we selecting each time?</td>
      <td>Defined unit: person, household, school, cluster, etc.[web:3][web:7][web:9]</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Choose sampling method</td>
      <td>How will we select them?</td>
      <td>Chosen design (e.g., simple random, stratified, cluster, non-probability).[web:1][web:5][web:7][web:9]</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Determine sample size</td>
      <td>How many do we need?</td>
      <td>Planned number of units balancing precision and feasibility.[web:1][web:5][web:8][web:9]</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Develop sampling plan</td>
      <td>What exact procedures will we follow?</td>
      <td>Written protocol for accessing the frame and selecting units.[web:2][web:5][web:8][web:9]</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Select sample & execute</td>
      <td>How do we implement and collect data?</td>
      <td>Actual selected sample and collected dataset.[web:1][web:3][web:5][web:9]</td>
    </tr>
  </tbody>
</table>

TL;DR: The steps in the process of sampling typically include: defining the target population, specifying the sampling frame and sampling unit, choosing a sampling method, determining the sample size, developing a detailed sampling plan, and finally selecting the sample and collecting data according to that plan.

Information gathered from public forums or data available on the internet and portrayed here.