The framing effect occurs when people react differently to something depending on whether it is presented as positive or negative. In other words, our decision is influenced by how the information is presented rather than what is being said.
Example: Framing effectWhile doing your groceries, you see two different beef products. Both cost and weigh exactly the same. One is labeled ‘80% lean’ and the other ‘20% fat’.
Comparing the two, you feel that 20% fat sounds like an unhealthy option, so you choose the 80% lean option. In reality, there is no difference between the two products, but one sounds more appealing than the other due to the framing effect.
The framing effect can impact our decision-making skills and can be observed in a number of contexts and fields (e.g., psychology, political communication, and marketing).
The availability heuristic occurs when we judge the likelihood of an event based on how easily we can recall similar events. If we can vividly remember instances of that event, we deem it to be more common than it actually is.
Example: Availability heuristicWhen asked if falling airplane parts or shark attacks are a more likely cause of death in the United States, most people would say shark attacks. In reality, the chances of dying from falling airplane parts are 30 times greater than the chances of being killed by a shark.
People overestimate the risk of shark attacks because there are far more news stories and movies about them. As a result, images of shark attacks are easier to bring to mind. If you can quickly think of multiple examples of something happening, then you are tricked into thinking it must happen often.
Due to the availability heuristic, our perception of reality can be distorted. This can lead to poor decision-making (especially when assessing risks) and to a few types of research bias, including recall bias.
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 judgements we make will be coloured by this first impression.
Example: Halo effectThe 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.
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 biasStudies 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.
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 generalisability 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.
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 judgement and affect how clearly we perceive situations, people, or potential risks.
Example: Cognitive biasOne 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.
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 biasYou 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.
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 biasSuppose 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.
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 phenomenonSuppose 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.
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 biasLet’s say you want to investigate the effect of education on people’s salaries. In order to correctly analyse 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.
