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 biasSuppose 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.
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 effectYou 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.
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 meanRegression 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.
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: GeneralizabilitySuppose 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.
Survivorship bias occurs when researchers focus on individuals, groups, or cases that have passed some sort of selection process while ignoring those who did not. Survivorship bias can lead researchers to form incorrect conclusions due to only studying a subset of the population. Survivorship bias is a type of selection bias.
Survivorship bias exampleA hospital is conducting research on trauma patients admitted to the ER, seeking to find out which procedures work best. However, researchers can only begin their studies if a patient is stable enough to give consent. Because the trial excludes everyone who didn’t survive their injuries or is too sick to consent, survivorship bias may occur.
In addition to being a common form of research bias, survivorship bias can also lead to poor decision-making in other areas, such as finance, medicine, and business.
Selection bias refers to situations where research bias is introduced due to factors related to the study’s participants. Selection bias can be introduced via the methods used to select the population of interest, the sampling methods, or the recruitment of participants. It is also known as the selection effect.
Example: Selection biasHealth studies that recruit participants directly from clinics miss all the cases who don’t attend those clinics or seek care during the study.
Due to this, the sample and the target population may differ in significant ways, limiting your ability to generalize your findings.
Selection bias may threaten the validity of your research, as the study population is not representative of the target population.
The Pygmalion effect refers to situations where high expectations lead to improved performance and low expectations lead to worsened performance. Although the Pygmalion effect was originally observed in the classroom, it also has been applied to in the fields of management, business, and sports psychology.
Example: Pygmalion effectYou want to research the influence of two storytelling methods on the vocabulary size improvement of children. To test this, the children are either given 20 minutes of storytelling from their teacher or 20 minutes of computerized storytelling.
You strongly believe the human aspect is needed to aid in the vocabulary development of children. You encourage the children in that group to pay attention and be excited, whereas you don’t show this behavior to the computer group.
The children in the first group are now paying more attention and feeling better about themselves than children in the other group, potentially leading to a Pygmalion effect.
The Pygmalion effect is also known as the Rosenthal effect, after the researcher who first observed the phenomenon.
The Hawthorne effect refers to people’s tendency to behave differently when they become aware that they are being observed. As a result, what is observed may not represent “normal” behavior, threatening the internal and external validity of your research.
The Hawthorne effect is also known as the observer effect and is closely linked with observer bias.
Example: Hawthorne effectYou are researching the smoking rates among bank employees as part of a smoking cessation program. You collect your data by watching the employees during their work breaks.
If employees are aware that you are observing them, this can affect your study’s results. For example, you may record higher or lower smoking rates than are genuinely representative of the population under study.
Like other types of research bias, the Hawthorne effect often occurs in observational and experimental study designs in fields like medicine, organizational psychology, and education.
Confirmation bias is the tendency to seek out and prefer information that supports our preexisting beliefs. As a result, we tend to ignore any information that contradicts those beliefs. Confirmation bias is often unintentional but can still lead to poor decision-making in (psychology) research and in legal or real-life contexts.
Example: Confirmation biasDuring presidential elections, people tend to seek information that paints the candidate they support in a positive light, while dismissing any information that paints them in a negative light.
This type of research bias is more likely to occur while processing information related to emotionally charged topics, values, or deeply held beliefs.
Inclusion and exclusion criteria determine which members of the target population can or can’t participate in a research study. Collectively, they’re known as eligibility criteria, and establishing them is critical when seeking study participants for clinical trials.
This allows researchers to study the needs of a relatively homogeneous group (e.g., people with liver disease) with precision. Examples of common inclusion and exclusion criteria are:
Study-specific variables: Type and stage of disease, previous treatment history, presence of chronic conditions, ability to attend follow-up study appointments, technological requirements (e.g., internet access)
Control variables: Fitness level, tobacco use, medications used
