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What is the best sampling method for a large population?

When conducting research on a large population, it is often not feasible to survey every member of the population due to constraints of time, resources and access. Sampling methods allow researchers to select a subset or sample of the population that is representative of the whole population. The sample results can then be generalized to make inferences about the overall population. Selecting the most appropriate sampling method is crucial to ensure the sample accurately reflects the population. This article will examine some of the key considerations when choosing a sampling method for a large population.

What is sampling and why is it used?

Sampling refers to the process of selecting a subset of individuals from within a population to estimate characteristics of the whole population. It is used to draw inferences about the population based on the sample, because surveying the entire population would be impractical or impossible.

The key reasons sampling is used include:

  • Lower costs – Surveying a sample rather than the entire population requires fewer resources.
  • Greater speed – Collecting data from a sample takes less time.
  • Higher accuracy – A smaller sample makes it easier to ensure proper data collection procedures are followed.
  • Accessibility – It may not be possible to access or locate all members of the target population.

If a sample is representative of the population, the results obtained for the sample can be generalized to the population within a calculated margin of error. Sampling is widely used in public opinion polls, market research, quality assurance inspections and political polling.

Key factors in selecting a sampling method

When selecting a sampling method, key factors to consider include:

  • Population size – A very large population requires a different approach compared to a small one.
  • Heterogeneity – If the population is very diverse, more complex sampling is required.
  • Cost/time constraints – The available budget and time impact feasibility.
  • Nature of research – Some studies require probability/representative samples.
  • Data analysis – The intended use of results affects sampling needs.

Defining the target population, required sample size, available resources, and analysis techniques before selecting a sampling method is important.

Overview of sampling methods

Sampling methods can be divided into two main categories:

  • Probability sampling – Each member has a known chance of being selected. Allows estimating sampling error.
  • Non-probability sampling – Members are selected in a non-random manner. Sampling error cannot be calculated.

Within these categories are several specific sampling techniques:

Probability sampling

  • Simple random – All members have equal chance of selection using a random process.
  • Systematic – Select every nth member from a list. Gives even spread.
  • Stratified – Divide into sub-groups then randomly sample from each strata.
  • Cluster – Divide into natural clusters, randomly sample a set number of clusters.

Non-probability sampling

  • Convenience – Select the most accessible members.
  • Judgement – Researcher selects based on judgement.
  • Quota – Select a group that matches the population on key factors.
  • Snowball – Existing members recruit more respondents.

Determining which approach is suitable depends on the specific goals, population, and resources of the research project.

Simple random sampling

In simple random sampling, all members of the population have an equal chance of being selected as part of the sample. It requires creating a sampling frame listing all members of the population, then using a random selection process to choose members from the frame until the desired sample size is reached. All selections are independent.

The benefits of simple random sampling include:

  • Straightforward to implement if sampling frame exists.
  • Allows calculating a margin of error and confidence levels.
  • Eliminates selection bias and provides representative sample.

Simple random sampling becomes difficult with very large populations. Creating a sampling frame with millions of entries is impractical. It can also lead to high variability between samples.

How to conduct simple random sampling

The process involves:

  1. Define population and required sample size using sample size calculators.
  2. Create comprehensive sampling frame listing all members.
  3. Use a computerized random number generator or lottery method to select members.
  4. Continue selections until desired sample size is reached.
  5. Contact and collect data from sampled members.

Simple random sampling is the most straightforward probability sampling method. But alternative techniques may be better suited for large populations.

Systematic sampling

Systematic sampling selects members at regular intervals from an ordered sampling frame. After randomly choosing a starting point, every nth member is included. This gives a evenly distributed sample across the whole population.

The advantages of systematic sampling include:

  • Simpler than true random sampling once interval is determined.
  • Can be efficiently distributed across geographic areas.
  • Guarantees even sample distribution.

However, periodicity in the ordering could lead to biased samples if the sample interval aligns with a pattern in the list. Using multiple random starting points can avoid this.

Implementing systematic sampling

This involves:

  1. Obtain sampling frame and determine required sample size.
  2. Calculate sampling interval by dividing population by sample size.
  3. Select random starting point between 1 and interval.
  4. Choose every nth member from the starting point.
  5. Repeat until desired sample size is reached.

For example, from a population of 5000, a sample of 100 would have an interval of 5000/100 = 50. If the random start was 17, the sample would be members 17, 67, 117…until 100 are selected.

Stratified sampling

Stratified sampling involves dividing the population into homogeneous subgroups called strata, then randomly sampling from each stratum. This ensures the sample accurately reflects the stratification of the population.

The main advantages are:

  • Captures key sub-groups that may otherwise be under-represented.
  • Can provide greater precision than simple random sampling.
  • Allows subgroup analysis and comparisons.

But stratified sampling requires thorough understanding of the population and variables. And appropriate stratification variables must be available for all members.

Steps in stratified sampling

The key stages include:

  1. Identify relevant strata and divide population accordingly.
  2. Obtain sampling frame for each stratum.
  3. Determine sample size from each stratum using proportional or optimal allocation.
  4. Randomly select required number of members from each stratum.
  5. Combine selections across all strata for complete sample.

Adequately stratifying the population takes considerable preparatory work, but yields representative, cross-segment samples.

Cluster sampling

Cluster sampling involves dividing the population into clusters based on naturally occurring groupings, randomly sampling a set number of clusters, then surveying all members within the selected clusters.

The main pros of cluster sampling are:

  • Clusters provide an convenient sampling frame.
  • Reduces travel and administration costs.
  • Feasible for large populations or scattered areas.

However, a larger sample size is required than other probability methods to achieve the same level of precision. Inter-cluster variability also needs to be accounted for.

Implementing cluster sampling

The key steps are:

  1. Divide population into clusters based on logical divisions.
  2. Identify the number of clusters to be sampled.
  3. Randomly select required number of clusters.
  4. Collect data from all members within sampled clusters.

Clusters may be based on locations, time-periods, or logical groupings. All members in the randomly selected clusters are sampled. This simplifies fieldwork but requires larger sample sizes.

Convenience sampling

Convenience sampling involves selecting the most accessible members of the population to participate. It makes no attempt to be statistically representative.

Advantages of convenience sampling:

  • Very fast and extremely low cost.
  • Useful for pilot studies or hypothesis generation.
  • Can reach niche groups using targeted adverts or intercept surveys.

However, results cannot be generalized since sample is unlikely to represent population. Selection bias is also a major issue.

Implementing convenience sampling

This simply requires:

  1. Identifying most easily accessible members.
  2. Contacting and recruiting participants.
  3. Collecting data until required sample size is reached.

No formal randomization is involved. Convenience sampling is quick but should not be used for definitive research.

Quota sampling

Quota sampling aims to produce a sample that mirrors known characteristics of the population, such as age, gender or class demographics. Quotas for key parameters are set, then convenience or judgement sampling used to fill the quotas.

Advantages of quota sampling:

  • Can rapidly achieve sample with desired characteristics.
  • Useful where list of population members unavailable.
  • Cheaper than probability methods for large populations.

However, within quotas, selections are non-random. Quota sampling relies heavily on the researchers ability to adequately categorize the population.

Quota sampling process

The main steps are:

  1. Determine relevant quota variables and quota percentages/sizes.
  2. Recruit participants that meet quota criteria through available means.
  3. Continue recruitment until all quotas are filled.
  4. Collect data from sample.

Quota sampling matches the sample to the population based on selected factors. But other sample characteristics may be unrepresentative.

Snowball sampling

Snowball sampling utilizes referrals from existing members of the target population to recruit additional participants. An initial set of participants are identified, then asked to recommend others.

Key benefits of snowball sampling:

  • Useful for hard-to-reach or hidden populations.
  • Low-cost and logistically simpler than probability methods.
  • Leverages insider knowledge to locate elusive members.

However, samples risk missing isolated members and may not represent overall population diversity. Over-reliance on central nodes can also introduce bias.

Snowball sampling process


  1. Identifying some existing members of the target population.
  2. Collecting data from this initial sample.
  3. Asking these members to recruit others by referral.
  4. Repeating data collection, referral process until desired sample size reached.

Snowball sampling uses social networks to uncover hard-to-find groups. But underlying network structures influence the sample formed.

Selecting the best sampling method

For large populations, the main probability sampling approaches suitable are:

  • Simple random – When complete sampling frame is available. Gives most representative sample.
  • Systematic – When ordered list available. More practical than simple random.
  • Stratified – If appropriate stratification factors known. Gives precision for subgroups.
  • Cluster – Most feasible for large dispersed populations. Cost-effective but less precise.

The best choice depends primarily on:

  • Availability of sampling frame or inherent clusters.
  • Cost and logistical constraints.
  • Whether subgroup analysis needed.
  • Required precision level and confidence intervals.

Non-probability methods can be useful despite limitations. But for formal studies, probability sampling is generally recommended to support statistical analysis.


For a large population, the optimal sampling approach depends on the research objectives, data analysis needs, available resources, and characteristics of the target population. While non-probability sampling offers logistical convenience, probability sampling provides representative data that can be reliably analyzed and generalized.

Simple random sampling is theoretically ideal, but often impractical beyond smaller populations. Systematic sampling offers an easier approach if the population can be ordered. Stratified sampling is effective for capturing key sub-groups within the population. Cluster sampling provides a practical option for dispersed populations.

The sampling method should be selected based on the specifics of the research context and goals. Adequately accounting for cost, population accessibility, analysis needs and project timelines helps determine the most appropriate sampling technique.

Regardless of approach, the sample size should be carefully calculated using statistical principles to ensure meaningful, valid results that reflect the wider population. Applying rigorous sampling methodology is crucial for producing generalizable insights through survey research.