The 5 most common sampling mistakes (2023)

What are sampling errors and why are they important?

To understand what sampling errors are, you first need to know a bit about sampling and what it means.research survey. (If you already know all about sampling, feel free to continue to the next section.)

When you take a survey, you are generally interested in a much larger group of people than you can reach. The practical solution is to draw a representative sample, a group that is representative of the actual population you want to study.

To make sure your example is a fair representation, there are a few you should followBest Practices for Survey Sampling. The best known of them isfind the correct sample size. (If the sample size is too large, you work too hard for no significant gain. If the sample size is too small, you cannot be sure that your sample is representative of the actual population.)

But there is more to good sampling than simply finding the correct sample size. Because of this, it's important to understand both sampling and non-sampling errors so you can prevent them from causing problems in your research.

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Free eBook: Minimizing Sampling and Non-Sampling Errors

Non-sampling error vs. sampling error: definitions

Somewhat confusingly, the term "sampling error" does not imply errors researchers made in selecting or working with a sample. Issues such as choosing the wrong people, allowing bias, or not anticipating that participants will self-select or not respond: These are non-sampling errors.

Definition of sampling error

Sampling error, on the other hand, is the difference between the sample mean and the mean of the entire population, so it only occurs when working with representative samples. It is the inevitable gap between your sample and the true population value.

AsThe OECD explains, the entire population is never perfectly represented by a sample because the population is larger and more complete. In this sense, a sampling error occurs every time you sample. It is not human error and cannot be completely avoided.

Interestingly, it is usually not possible to quantify the amount of sampling error in a study because, by definition, it does not measure relevant data for the entire population.

However, you can reduce sampling errors by following best practices; more on that below.

Is the sampling error equal to the standard error?

Standard error is a popular way to measure sampling error. Expresses the magnitude of the sampling error so that it can be communicated and understood. Sampling error is the concept, standard error is the way it is measured.

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What about the standard deviation?

The standard error is a type of standard deviation. It is the difference between the sample mean and the mean of the entire population. In other words, it is the amount by which the sample mean would change if you repeated the sampling process several times.

And the confidence intervals?

A confidence interval expresses how much error there is in your results, that is, how sure you can be that they are correct. Confidence intervals express the upper and lower limits of your margin of error. When the margin of error is small, confidence is greater. The confidence interval of a result is often expressed as a percentage, e.g. 95% or 99%.

Definition of non-sampling error

Non-sampling errors can occur even if you do not sample. That means they should be avoided, whether you're working with a representative sample (for example, in a national survey) or taking a full census of your entire population (for example, if you're doing a survey).Employee experience surveyswith their employees).

Non-sampling errors occur when there are problems with the sampling method or the way the survey is designed or conducted. We will cover some of the worst offenders later in this article.

The 5 most common sampling mistakes (1)

Examples of sampling and non-sampling errors

1. Population specification errors (without sampling errors)

This error occurs when the researcher does not understand who to interview. For example, imagine a survey on the consumption of breakfast cereals in families. ask who? It can be the whole family, the person who does most of the shopping, or the children. The buyer can make the purchase decision, but children influence the choice of cereal.

This type of non-sampling error can be avoided by thoroughly understanding your research question before you start building.Proofor choice of respondents.

2. Sample frame error (non-sampling error)

A sampling frame error occurs when the wrong subpopulation is used to select a sample, such that it significantly underrepresents the entire population. In the 1936 United States presidential election, a classic framing error occurred between Roosevelt, the Democratic nominee, and Landon, of the Republican Party. The sample frame came from automobile records and telephone directories. In 1936, many Americans did not own automobiles or telephones, and those that did were mostly Republicans. The results incorrectly predicted a Republican victory.

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The flaw here lies in the way a sample was selected. The bias was unknowingly introduced because the researchers did not anticipate that only certain types of people would appear on their respondent list, and sections of the population of interest were excluded. A modern equivalent could be the use of mobile phone numbers and thus inadvertently missing adults who do not have a mobile phone, such as B. Older people or people with severe learning difficulties.

Framing errors can also occur when respondents from outside the population of interest are incorrectly included. Suppose a researcher is conducting a national study. Her list may come from a geographic map area that inadvertently includes a small corner of foreign territory and therefore contains respondents irrelevant to the scope of the study.

3. Selection error (non-sampling error)

Selection errors occur when respondents choose their own participation in the study; Only those who are interested respond. It can also be introduced as a non-random sampling error by the researcher. For example, if a researcher requests a response on social media, he will receive responses from people she knows, and of those people, only the most helpful or kind people will respond. They are not a random sample of the entire population.

Selection errors can be controlled by improving data collection methods and by making additional efforts to achieve participation. A typical survey process includes initiating pre-survey contacts to solicit collaboration, the actual survey, and post-survey follow-up. If no response is received, a second interview request follows and possibly interviews using alternative methods such as telephone or person-to-person.

4. Non-response (non-sampling error)

Response errors occur when respondents differ from non-respondents. For example, let's say yesmarket researchbefore launching a new product. You may get a disproportionate share of your existing customers as they know who you are and miss out on hearing from a larger group of people who aren't buying from you yet. As with selection error, this results in a non-random sample that misrepresents the entire population.

A response error may occur because the potential respondent was not contacted or refused to respond. The magnitude of this non-response error can be verified by follow-up surveys using alternative modes.

5. Sampling error

As described above, sampling error occurs due to variations in the number or representativeness of the responding sample. Sampling errors can be controlled and reduced through (1) careful sample design, (2) large enough samples (see our online sample calculator), and (3) multiple contacts to ensure a representative response.

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The 5 most common sampling mistakes (2)

Be aware of these sampling and non-sampling errors so that you can avoid them in your research.

How can sampling and non-sampling error be used to improve market research?

By understanding how sampling errors and the different types of non-sampling errors work, you will be able to produce better, reliable results for your business market research.

Data skewed by common non-sampling errors or unnecessary sampling errors can create confusion in your organization, as different results from different studies may conflict with each other.

Worse yet, poor-quality data can lead to incorrect predictions, as in the Roosevelt election, when errors in the sampling frame led to false confidence in a Republican victory. Translate this problem into a business case and you could end up misjudging your market and making costly mistakes.

How Qualtrics can help

Working with samples and avoiding statistical errors can quickly become a complex task that requires expert knowledge and specialized personnel. Fortunately, there are solutions you can integrate into your business that don't require any of these things.

With the Qualtrics market research platform, you can take advantage of market-leading statistical tools that produce reports and data that laymen can easily understand. With predictions and insights expressed in simple phrases, you can use them to communicate insights at all levels of your organization and make important business decisions with confidence.

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In addition to world-class software features, you also have access to research and expert services that can help you with everything from survey strategy and creation to panel management and execution.


What are the five sampling errors? ›

In general, sampling errors can be placed into four categories: population-specific error, selection error, sample frame error, or non-response error. A population-specific error occurs when the researcher does not understand who they should survey.

What are the common mistakes made in sampling? ›

Some of the most common sampling errors are sample frame errors, selection errors, population specification errors, and non-response errors.

What are the 5 main types of sampling? ›

Five Basic Sampling Methods
  • Simple Random.
  • Convenience.
  • Systematic.
  • Cluster.
  • Stratified.
Jun 30, 2019

What is sampling describe any 5 methods? ›

In non-probability sampling, the sample is selected based on non-random criteria, and not every member of the population has a chance of being included. Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.

What are the 5 factors affecting sample selection? ›

The factors affecting sample sizes are study design, method of sampling, and outcome measures – effect size, standard deviation, study power, and significance level.

What is sampling error answer? ›

Sampling error is the difference between a population parameter and a sample statistic used to estimate it. For example, the difference between a population mean and a sample mean is sampling error. Sampling error occurs because a portion, and not the entire population, is surveyed.…

What is an example of incorrect sampling? ›

Sample frame error: Sampling frame errors arise when researchers target the sub-population wrongly while selecting the sample. For example, picking a sampling frame from the telephone white pages book may have erroneous inclusions because people shift their cities.

What are the most common cause to have a sample error? ›

The main cause of sampling error is simple: it occurs when statistical characteristics of a certain part or a subset of a population are incorrectly assumed to apply to the entire population.

What is an example of a sampling error in research? ›

Categories of Sampling Errors

For example, for a survey of breakfast cereals, the population can be the mother, children, or the entire family. Selection Error – Occurs when the respondents' survey participation is self-selected, implying only those who are interested respond.

What are the five steps of sampling? ›

The five steps to sampling are:
  • Identify the population.
  • Specify a sampling frame.
  • Specify a sampling method.
  • Determine the sample size.
  • Implement the plan.
May 25, 2022

What are the 4 types of random sampling? ›

There are four primary, random (probability) sampling methods – simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

What are the 4 sampling strategies? ›

Four main methods include: 1) simple random, 2) stratified random, 3) cluster, and 4) systematic. Non-probability sampling – the elements that make up the sample, are selected by nonrandom methods. This type of sampling is less likely than probability sampling to produce representative samples.

What are six sampling methods? ›

6 Techniques for Market Research Sampling
  1. True Random Sampling.
  2. Systematic Sampling.
  3. Stratified Sampling.
  4. Quota Sampling.
  5. Cluster Sampling.
  6. Area Sampling.
  7. Choosing the Right Sampling Technique Your Market Research.

What are 3 of the 5 factors that affect population? ›

Birth rate, death rate and migration rate affect the population of a country. When the birth rate increases and the death rate decreases, the population of a country increases.

What are the 3 factors of sampling? ›

In general, three or four factors must be known or estimated to calculate sample size: (1) the effect size (usually the difference between 2 groups); (2) the population standard deviation (for continuous data); (3) the desired power of the experiment to detect the postulated effect; and (4) the significance level.

What are sampling and non sampling errors? ›

Non-sampling error refers to an error that arises from the result of data collection, which causes the data to differ from the true values. It is different from sampling error, which is any difference between the sample values and the universal values that may result from a limited sampling size.

What is a sampling error quizlet? ›

Sampling error is the error that results from using a sample to estimate information about a population. This type of error occurs because a sample gives incomplete information about a population.

How do I find sampling error? ›

How to calculate sampling error
  1. Record the sample size. ...
  2. Find the standard deviation of the population. ...
  3. Determine your confidence level. ...
  4. Calculate the square root of the sample size. ...
  5. Divide the standard deviation value by the square root value. ...
  6. Multiply the result by the confidence level.
Oct 18, 2021

What is a Type 1 error in sampling? ›

A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.

What are random sampling errors? ›

A sampling error in cases where the sample has been selected by a random method. It is common practice to refer to random sampling error simply as “sampling error” where the random nature of the selective process is understood or assumed.

What are the two causes of sampling errors? ›

Sampling error is affected by a number of factors including sample size, sample design, the sampling fraction and the variability within the population. In general, larger sample sizes decrease the sampling error, however this decrease is not directly proportional.

What are the 5 sources of error? ›

Common sources of error include instrumental, environmental, procedural, and human. All of these errors can be either random or systematic depending on how they affect the results.

What are the three most common types of errors? ›

Types of Errors
  • Gross Errors.
  • Random Errors.
  • Systematic Errors.

What are common errors in statistics? ›

Types of mistakes

Expecting too much certainty. Misunderstandings about probability. Mistakes in thinking about causation. Problematical choice of measure.

What is chance error in sampling? ›

Chance Error – Also called “sampling error” and comes from the fact that the sample is only part of the whole. Chance Variability – The notion that chance error varies from sample to sample, even when samples are from the same population. Expected Value – This value for a sample equals the population percentage.

What are the basics of sampling? ›

Sampling is a process in statistical analysis where researchers take a predetermined number of observations from a larger population. The method of sampling depends on the type of analysis being performed, but it may include simple random sampling or systematic sampling.

What are the main elements of sampling? ›

In other words, the sampling process involves three main elements – selecting the sample, collecting the information, and also making inferences about the population.

What are the golden rules of sampling? ›

Good Sampling Practice

The “Golden Rules” for sub-sampling put forth by Allen [1] simply state that the sample(s) should be taken when the powder is in motion (i.e. a powder stream), and the entire cross section of the entire stream shall be sampled many times.

What are the steps in developing a sampling plan 1/5 *? ›

The steps involved in developing a sampling plan are:
  1. identify the parameters to be measured, the range of possible values, and the required resolution.
  2. design a sampling scheme that details how and when samples will be taken.
  3. select sample sizes.
  4. design data storage formats.
  5. assign roles and responsibilities.

What is example of sampling? ›

For example, a researcher intends to collect a systematic sample of 500 people in a population of 5000. He/she numbers each element of the population from 1-5000 and will choose every 10th individual to be a part of the sample (Total population/ Sample Size = 5000/500 = 10).

What is the 5th steps in the research process *? ›

Step 5 – Report Research Findings

The final step is to report the research findings to those who need the data to make decisions. The findings should be presented in a comprehensible format so that they can be readily used in the decision-making process.

What are the 5 types of non-probability sampling? ›

The commonly used non-probability sampling methods include the following.
  • Convenience or haphazard sampling. ...
  • Volunteer sampling. ...
  • Judgement sampling. ...
  • Quota sampling. ...
  • Snowball or network sampling. ...
  • Crowdsourcing. ...
  • Web panels. ...
  • Advantages and disadvantages of non-probability sampling.
Sep 2, 2021

How many main types of sampling are there? ›

There are two main types of sampling: probability sampling and non-probability sampling. The main difference between the two types of sampling is how the sample is selected from the population.

What is the most common sampling strategy? ›

There are numerous ways of getting a sample, but here are the most commonly used sampling methods:
  1. Random Sampling. ...
  2. Stratified Sampling. ...
  3. Systematic Sampling. ...
  4. Convenience Sampling. ...
  5. Quota Sampling. ...
  6. Purposive Sampling.

Which is the most commonly used sampling technique *? ›

Methods of sampling

To ensure reliable and valid inferences from a sample, probability sampling technique is used to obtain unbiased results. The four most commonly used probability sampling methods in medicine are simple random sampling, systematic sampling, stratified sampling and cluster sampling.

What is the best way to sample data? ›

The most straightforward way to sample data is with simple random sampling. Essentially, the subset is built of observations that were chosen from a larger set purely by chance; Each observation has the same chance of being selected from the larger set. Simple random sampling is extremely simple and easy to implement.

What is a sampling error quizlet Chapter 7? ›

Terms in this set (29) What is a sampling error? a. The natural error that exists between a sample and its corresponding population.

What factors should be considered when determining the sampling method? ›

  1. the reasons for and objectives of sampling.
  2. the relationship between accuracy and precision.
  3. the reliability of estimates with varying sample size.
  4. the determination of safe sample sizes for surveys.
  5. the variability of data.
  6. the nature of stratification and its impact on survey cost.

What are Type 1 and Type 2 errors in sampling? ›

A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.

What is an example of a sample error? ›

Examples of Sampling Errors

It occurs when the members of the sample are unrepresentative of the population. If you have a survey that samples from one part of the country and does not represent the other parts of the country, you have a survey with sampling bias.

What are the types of errors in statistics? ›

Data can be affected by two types of error: sampling error and non-sampling error.

What are Type 1 2 and 3 errors? ›

Type I error: "rejecting the null hypothesis when it is true". Type II error: "failing to reject the null hypothesis when it is false". Type III error: "correctly rejecting the null hypothesis for the wrong reason". (1948, p.

What is a Type 3 error in statistics? ›

A type III error is where you correctly reject the null hypothesis, but it's rejected for the wrong reason. This compares to a Type I error (incorrectly rejecting the null hypothesis) and a Type II error (not rejecting the null when you should).

What are Type 2 errors in statistics? ›

Type 2 errors happen when you inaccurately assume that no winner has been declared between a control version and a variation although there actually is a winner. In more statistically accurate terms, type 2 errors happen when the null hypothesis is false and you subsequently fail to reject it.

What are the 3 types of errors? ›

Types of Errors
  • (1) Systematic errors. With this type of error, the measured value is biased due to a specific cause. ...
  • (2) Random errors. This type of error is caused by random circumstances during the measurement process.
  • (3) Negligent errors.

What are the three 3 types of errors? ›

Types of Errors
  • Gross Errors.
  • Random Errors.
  • Systematic Errors.

What is the most common type of errors? ›

The two most common types of errors made by programmers are syntax errors and logic errors Let X denote the number of syntax errors and Y the number of logic errors on the first run of a program.

What are the types of common errors? ›

  • Sentence fragments. ...
  • Sentence sprawl. ...
  • Misplaced and dangling modifiers. ...
  • Faulty parallelism. ...
  • Unclear pronoun reference. ...
  • Incorrect pronoun case. ...
  • Omitted commas. ...
  • Superfluous commas.

How do you find the sampling error? ›

How to calculate sampling error
  1. Record the sample size. ...
  2. Find the standard deviation of the population. ...
  3. Determine your confidence level. ...
  4. Calculate the square root of the sample size. ...
  5. Divide the standard deviation value by the square root value. ...
  6. Multiply the result by the confidence level.
Oct 18, 2021

What is random error in sampling? ›

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).


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