What Is Nonresponse Bias? | Definition & Example

Nonresponse bias happens when those unwilling or unable to take part in a research study are different from those who do.

In other words, this bias occurs when respondents and nonrespondents categorically differ in ways that impact the research. As a result, the sample is no longer representative of the population as a whole.

Example: Nonresponse bias
Suppose you are researching workload among managers in a supermarket chain. You decide to collect your data via a survey. Due to constraints on their time, managers with the largest workload are less likely to answer your survey questions.

This may lead to a biased sample, as those most likely to answer are the managers with less busy schedules. Consequently, your results are likely to show that manager workload in the supermarket chain is not very high—something that may not, in fact, be true.

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The Baader–Meinhof Phenomenon Explained

The Baader–Meinhof phenomenon refers to the false impression that something happens more frequently than it actually does. This often occurs when we learn something new. Suddenly, this new thing seems to appear more frequently, when in reality it’s only our awareness of it that has increased.

Example: Baader–Meinhof phenomenon
Suppose that you decide to buy a car, and you have set your mind on a specific blue model. In the next few days, you see that blue color wherever you go. It feels like suddenly, everyone is driving a car in that color.

The Baader–Meinhof phenomenon is also known as the frequency illusion or recency illusion. While it’s mostly harmless, it can affect our ability to recall events correctly, or cause us to see patterns that aren’t actually there.

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What Is Omitted Variable Bias? | Definition & Examples

Omitted variable bias occurs when a statistical model fails to include one or more relevant variables. In other words, it means that you left out an important factor in your analysis.

Example: Omitted variable bias
Let’s say you want to investigate the effect of education on people’s salaries. In order to correctly analyze this effect, you should also include ability in your model. Ability makes a student more successful than their peers in school, which may lead to a better job and a better salary after graduation.

If you don’t have a trustworthy measure of ability, you may have to exclude it from your model despite knowing that it’s an important variable.

In this case, excluding ability causes omitted variable bias. This may lead to an overestimation or under-estimation of the effect of your other variables.

As a result, the model mistakenly attributes the effect of the missing variable to the included variables. Exclusion of important variables can limit the validity of your study findings.

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What Is Publication Bias? | Definition & Examples

Publication bias refers to the selective publication of research studies based on their results. Here, studies with positive findings are more likely to be published than studies with negative findings.

Positive findings are also likely to be published quicker than negative ones. As a consequence, bias is introduced: results from published studies differ systematically from results of unpublished studies.

Example: Publication bias
In 2014, Franco et al. studied publication bias in the social sciences by analyzing a sample of 221 studies whose publication status was known. The sample was drawn from an archive called Time-sharing Experiments in the Social Sciences (TESS).

Because TESS proposals undergo rigorous peer review, the sample studies drawn from the archive were all considered to be of high quality. Additionally, researchers could see in this archive whether the studies were eventually published or not.

Studies were classified into three categories:

  1.  Strong – all or most hypotheses were supported
  2.  Null – all or most hypotheses were not supported
  3.  Mixed – representing the rest

The authors found that only 10 out of 48 null results were published, while 56 out of 91 studies with strongly statistically significant results made it into an academic journal.

In other words, there was a strong relationship between the results of a study and whether it was published, a pattern that indicates publication bias.

Publication bias can affect any scientific field, leading to a biased understanding of the research topic.

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What Is Recall Bias? | Definition & Examples

Recall bias refers to systematic difference in the ability of participant groups to accurately recall information. Observational studies that rely on self-reporting of past behaviors or events are particularly prone to this type of bias.

Example: Recall bias
Parents whose children have developed asthma are likely to be quite concerned about what may have contributed to their child’s condition.

As a result, if asked by a researcher, these parents are more likely to recall relevant details, such as changes in their children’s breathing when active or resting, than parents of children without any health issues.

They’ve also already associated possible triggers, such as certain foods, environments, or other allergens, with their child’s asthma. This difference in the ability to recall information results in recall bias.

Recall bias threatens the internal validity and credibility of studies using self-reported data.

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What Is Response Bias? | Definition & Examples

Response bias refers to several factors that can lead someone to respond falsely or inaccurately to a question. Self-report questions, such as those asked on surveys or in structured interviews, are particularly prone to this type of bias.

Example: Response bias
A job applicant is asked to take a personality test during the recruitment process. One of the questions is “Do you like meeting new people?”

The applicant thinks that, since this is a customer service job, the company is probably looking for someone who enjoys meeting new people. Despite being an introvert at heart, the applicant answers “yes” in an attempt to increase their chances of being hired.

Because respondents are not actually answering the questions truthfully, response bias distorts study results, threatening the validity of your research. Response bias is a common type of research bias.

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What Is Ascertainment Bias? | Definition & Examples

Ascertainment bias occurs when some members of the target population are more likely to be included in the sample than others. Because those who are included in the sample are systematically different from the target population, the study results are biased.

Example: Ascertainment bias
Suppose you are investigating the ratio of people who identify as male or female in a certain area. You draw your sample from a housing project for elderly people. Because, statistically speaking, women tend to live longer than men, your results could be biased in favor of women, with women overrepresented in your sample.

Ascertainment bias is a form of selection bias and is related to sampling bias. In medical research, the term ascertainment bias is more common than the term sampling bias.

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What Is the Placebo Effect? | Definition & Examples

The placebo effect is a phenomenon where people report real improvement after taking a fake or nonexistent treatment, called a placebo. Because the placebo can’t actually cure any condition, any beneficial effects reported are due to a person’s belief or expectation that their condition is being treated.

Example: Placebo effect definition
You participate in a double-blind clinical trial on a new migraine medication. For the next month, each time you experience a migraine, you are instructed to take a pill and rate the pain intensity.

You feel that the pill relieves the symptoms, but at the end of the month you find out that you were given a placebo—and not the new medication. The perceived improvement you experienced was due to the placebo effect.

The placebo effect is often observed in experimental designs where participants are randomly assigned to either a control or treatment group. 

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Regression to the Mean | Definition & Examples

Regression to the mean (RTM) is a statistical phenomenon describing how variables much higher or lower than the mean are often much closer to the mean when measured a second time.

Regression to the mean is due to natural variation or chance. It can be observed in everyday life, particularly in research that intentionally focuses on the most extreme cases or events. It is sometimes also called regression toward the mean.

Example: Regression to the mean
Regression to the mean can explain the so-called “Sports Illustrated jinx.” This urban legend claims that athletes or teams that appear on the cover of the sports magazine will perform poorly in their next game.

Players or teams featured on the cover of SI have earned their place by performing exceptionally well. But athletic success is a mix of skill and luck, and even the best players don’t always win.

Chances are that good luck will not continue indefinitely, and neither can exceptional success.

In other words, due to RTM, a great performance is more likely to be followed by a mediocre one than another great one, giving the impression that appearing on the cover brings bad luck.

Regression to the mean is common in repeated measurements (within-subject designs) and should always be considered as a possible cause of an observed change. It is considered a type of information bias and can distort research findings.

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What Is Generalizability? | Definition & Examples

Generalizability is the degree to which you can apply the results of your study to a broader context. Research results are considered generalizable when the findings can be applied to most contexts, most people, most of the time.

Example: Generalizability
Suppose you want to investigate the shopping habits of people in your city. You stand at the entrance to a high-end shopping street and randomly ask passersby whether they want to answer a few questions for your survey.

Do the people who agree to help you with your survey accurately represent all the people in your city? Probably not. This means that your study can’t be considered generalizable.

Generalizability is determined by how representative your sample is of the target population. This is known as external validity.

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