Making statistics intuitive
Prospect Theory states that individuals place greater weight on losses than gains while making decisions. It is a descriptive model of how individuals make decisions involving risk and uncertainty proposed by Daniel Kahneman and Amos Tversky in 1979. Prospect theory describes how people evaluate and choose between different options. [Read more…] about Prospect Theory Overview & Examples
Self selection bias can occur when individuals choose to participate in a study, survey, or experiment. The bias exists when volunteers have different characteristics than those who do not participate. It is a form of sampling bias stemming from using a nonprobability sampling method, such as volunteer or convenience sampling. [Read more…] about Self Selection Bias Overview & Examples
Attrition bias in research occurs when study participants who drop out have characteristics that differ significantly from those who remain. This selective dropout can lead to skewed results and misinterpretations if the researchers don’t adequately address it. This threat is higher for longitudinal studies and those with relatively high attrition rates. [Read more…] about Attrition Bias: Definition & Examples
The conjunction fallacy is a cognitive bias that occurs when someone mistakenly believes that two events occurring together are more likely than either of the two events alone. In other words, it’s the mistaken belief that a precisely detailed, multifaced outcome is more likely to occur than a more generalized version of that outcome. [Read more…] about Conjunction Fallacy: Definition & Example
Base rate fallacy is a cognitive bias that occurs when a person misjudges an outcome by giving too much weight to case-specific details and overlooks crucial probability information that applies to all cases in a population. That vital probability is the outcome’s base rate of occurrence in the population. [Read more…] about Base Rate Fallacy Overview & Examples
Omitted variable bias (OVB) occurs when a regression model excludes a relevant variable. The absence of these critical variables can skew the estimated relationships between variables in the model, potentially leading to erroneous interpretations. This bias can exaggerate, mask, or entirely flip the direction of the estimated relationship between an independent and dependent variable. [Read more…] about Omitted Variable Bias: Definition, Avoiding & Example
The framing effect is a cognitive bias that distorts our decisions and judgments based on how information is presented or ‘framed.’ This effect isn’t about lying or twisting the truth. It’s about the same cold, hard facts making us think and act differently just by changing their packaging. [Read more…] about Framing Effect: Definition & Examples
The gambler’s fallacy is a cognitive bias that occurs when people incorrectly believe that previous outcomes influence the likelihood of a random event happening. The fallacy assumes that random events are “due” to balance out over time. It’s also known as the “Monte Carlo Fallacy,” named after a casino in Monaco where it was famously observed in 1913. [Read more…] about Gambler’s Fallacy: Overview & Examples
The representativeness heuristic is a cognitive bias that occurs while assessing the likelihood of an event by comparing its similarity to an existing mental prototype. Essentially, this bias involves comparing whatever we’re evaluating to a situation, prototype, or stereotype that we already have in mind. Our brains frequently weigh this comparison much more heavily than other relevant factors. This shortcut can be helpful in some cases, but it can also lead to errors in judgment and distorted thinking. [Read more…] about Representativeness Heuristic: Definition & Examples
A lurking variable is a variable that researchers do not include in a statistical analysis, but it can still affect the outcome. These variables can create problems by biasing your statistical results in any of the following ways:
Learn more about Spurious Correlations. [Read more…] about Lurking Variable: Definition & Examples
Anchoring bias is a cognitive bias that causes people to rely too heavily on the first piece of information they receive when making a decision. That information is their “anchor,” and it affects how they make decisions. Even when presented with additional information, people tend to give too much weight to the original anchor, leading to distortions in judgment and decision-making. Inaccurate adjustments from an anchor value can cause people to make erroneous final decisions and estimates. [Read more…] about Anchoring Bias: Definition & Examples
Self serving bias is a cognitive bias that refers to the tendency for individuals to take credit for their successes while blaming their failures on external factors. In other words, people tend to see themselves positively by attributing their accomplishments to their internal abilities and failures to things outside their control. [Read more…] about Self Serving Bias: Definition & Examples
Hindsight bias is a cognitive bias that creates the tendency to perceive past events as being more predictable than they actually were. It is that sneaky feeling that you “knew it all along,” even when that’s not true. This tendency is rooted in our desire to believe that we are intelligent and capable decision-makers, and it can cause various distortions in our thinking. [Read more…] about Hindsight Bias: Definition & Examples
The availability heuristic is a cognitive bias that causes people to rely too heavily on easily accessible memories when estimating probabilities and making decisions. This mental shortcut can distort our perception of how frequently certain events occur. [Read more…] about Availability Heuristic: Definition & Examples
The halo effect is a cognitive bias relating to our tendency to transfer a positive impression of one characteristic of a person or object to their other features. A classic example is that when you perceive someone as attractive, you are likely to assume they have other positive attributes, such as intelligence, kindness, and trustworthiness. [Read more…] about Halo Effect: Definition & Examples
Random error and systematic error are the two main types of measurement error. Measurement error occurs when the measured value differs from the true value of the quantity being measured. [Read more…] about Random Error vs Systematic Error
The Dunning-Kruger effect is a cognitive bias that causes people with low abilities or knowledge to overestimate themselves compared to others. Conversely, people with high skills tend to underestimate themselves. In short, it is a psychological phenomenon that distorts our self-evaluation. [Read more…] about Dunning Kruger Effect: Definition & Examples
Confirmation bias is the tendency to seek information confirming preexisting beliefs while ignoring information contradicting them. This bias can be particularly problematic when making important decisions, leading to flawed reasoning and inaccurate conclusions. It is a type of cognitive bias. [Read more…] about Confirmation Bias Definition and Examples
A cognitive bias is a systematic fault in thinking and decision-making that can affect our judgments and perceptions. These biases can arise due to our limited mental capacity, the complexity of the environment, and the influence of our prior experiences and beliefs. [Read more…] about Cognitive Bias: Definition & Examples
Selection bias occurs when researchers make decisions that cause a sample to be systematically different from the population of interest.
Selection bias can arise from various decisions, such as: