What Is the Halo Effect? | Definition & Examples

The halo effect occurs when our overall positive impression of a person, product, or brand is based on a single characteristic. If our first impression is positive, the subsequent judgments we make will be colored by this first impression.

Example: Halo effect
The halo effect is a common bias in performance appraisals. Supervisors often evaluate the overall performance of an employee on the basis of a single prominent characteristic. If an employee shows enthusiasm, this may influence the supervisor’s judgment, even if the employee lacks knowledge or competence in some areas. This may lead the supervisor to give them a higher rating due to their enthusiasm.

Because of the halo effect, one positive characteristic may overshadow all other aspects of the employee’s performance.

The halo effect can hamper our ability to think critically. It can be particularly problematic in decision-making contexts, such as job interviews and purchase decisions.

Continue reading: What Is the Halo Effect? | Definition & Examples

What Is Information Bias? | Definition & Examples

Information bias is a type of error that occurs when key study variables are incorrectly measured or classified. Information bias can affect the findings of observational or experimental studies due to systematic differences in how data is obtained from various study groups.

Example: Information bias
Studies of rare or newly discovered diseases that do not have uniform diagnostic criteria are at risk for information bias. In the absence of a common standard, people who do not have a disease may be classified as having it, and vice versa.

Information bias is also known as measurement bias or misclassification.

Continue reading: What Is Information Bias? | Definition & Examples

What Is Self-Selection Bias? | Definition & Example

Self-selection bias (also called volunteer bias) refers to the bias that can occur when individuals are allowed to choose whether they want to participate in a research study. Because participants often differ from nonparticipants in ways significant to the research, self-selection can lead to a biased sample and affects the generalizability of your results.

Example: Self-selection bias 
Suppose you are surveying high school English students. You ask them to rate the books they read throughout the academic year, but you make participation optional.

Because of that, students who either strongly enjoyed or hated the books are more likely to fill in the survey. Students who didn’t feel strongly about the books are less likely to participate in the survey.

As a result, your sample will comprise mostly those with strong opinions and will not be representative of all students. By allowing students to choose whether to participate, you have allowed self-selection bias to occur.

Continue reading: What Is Self-Selection Bias? | Definition & Example

What Is Cognitive Bias? | Definition, Types, & Examples

Cognitive bias is the tendency to act in an irrational way due to our limited ability to process information objectively. It is not always negative, but it can cloud our judgment and affect how clearly we perceive situations, people, or potential risks.

Example: Cognitive bias
One common manifestation of cognitive bias is the stereotype that women are less competent or less committed to their jobs. These stereotypes may linger in managers’ subconscious, influencing their hiring and promoting decisions. This, in turn, can lead to workplace discrimination.

Everyone is susceptible to cognitive bias, and researchers are no exception to that. Therefore, cognitive bias can be a source of research bias.

Continue reading: What Is Cognitive Bias? | Definition, Types, & Examples

What Is Undercoverage Bias? | Definition & Example

Undercoverage bias occurs when a part of the population is excluded from your sample. As a result, the sample is no longer representative of the target population. Non-probability sampling designs are susceptible to this type of research bias.

Example: Undercoverage bias
You are conducting research by randomly calling landline numbers. Because of your sampling method, individuals who only have mobile phones are not sampled. In this case, they are not merely undercovered, but not covered at all.

Undercoverage is a type of selection bias.

Continue reading: What Is Undercoverage Bias? | Definition & Example

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.

Continue reading: What Is Nonresponse Bias? | Definition & Example

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.

Continue reading: The Baader–Meinhof Phenomenon Explained

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.

Continue reading: What Is Omitted Variable Bias? | Definition & Examples

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.

Continue reading: What Is Publication Bias? | Definition & Examples

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.

Continue reading: What Is Recall Bias? | Definition & Examples