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bias sources

Framing Effect: Definition & Examples

By Jim Frost Leave a Comment

What is the Framing Effect?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Gambler’s Fallacy: Overview & Examples

By Jim Frost Leave a Comment

What is the Gambler’s Fallacy?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Representativeness Heuristic: Definition & Examples

By Jim Frost Leave a Comment

What is the Representativeness Heuristic?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Lurking Variable: Definition & Examples

By Jim Frost Leave a Comment

What is a Lurking Variable?

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:

  • Magnify the real effect.
  • Weaken the appearance of the relationship.
  • Change the sign of a correlation.
  • Mask an effect that actually exists.
  • Create phantom correlations where none exist!

Learn more about Spurious Correlations. [Read more…] about Lurking Variable: Definition & Examples

Filed Under: Basics Tagged With: bias sources, conceptual, experimental design

Anchoring Bias: Definition & Examples

By Jim Frost Leave a Comment

What is Anchoring Bias?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Self Serving Bias: Definition & Examples

By Jim Frost Leave a Comment

What is Self Serving Bias?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Hindsight Bias: Definition & Examples

By Jim Frost Leave a Comment

What is Hindsight Bias?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Availability Heuristic: Definition & Examples

By Jim Frost Leave a Comment

What is the Availability Heuristic?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Halo Effect: Definition & Examples

By Jim Frost 1 Comment

What is the Halo Effect?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Random Error vs Systematic Error

By Jim Frost Leave a Comment

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

Filed Under: Basics Tagged With: bias sources, conceptual, measurement error

Dunning Kruger Effect: Definition & Examples

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What is the Dunning Kruger Effect?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Confirmation Bias Definition and Examples

By Jim Frost Leave a Comment

What is Confirmation Bias?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Cognitive Bias: Definition & Examples

By Jim Frost Leave a Comment

What is Cognitive Bias?

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

Filed Under: Basics Tagged With: bias sources, conceptual

Selection Bias: Definition & Examples

By Jim Frost Leave a Comment

What is Selection Bias?

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:

  • Using an improper sampling method.
  • Making particular methodology and data choices.
  • Choosing a study design that affects the continued participation of subjects.

[Read more…] about Selection Bias: Definition & Examples

Filed Under: Basics Tagged With: bias sources, conceptual, experimental design, sampling methods

Undercoverage Bias: Definition & Examples

By Jim Frost Leave a Comment

What is Undercoverage Bias?

Undercoverage bias occurs when the population list from which the researchers select their sample (aka the sampling frame) does not include all population members. When that happens, the sample cannot contain the unlisted individuals, potentially producing a biased sample that doesn’t fully represent the population. [Read more…] about Undercoverage Bias: Definition & Examples

Filed Under: Basics Tagged With: bias sources, conceptual, experimental design

Nonresponse Bias: Definition & Reducing

By Jim Frost Leave a Comment

What is Nonresponse Bias?

Nonresponse bias occurs when people who do not participate in a survey or study have different characteristics or opinions than those who do participate. In this situation, the sample data overrepresent the subpopulations who tend to respond instead of reflecting the whole population. [Read more…] about Nonresponse Bias: Definition & Reducing

Filed Under: Basics Tagged With: bias sources, conceptual, experimental design

Simpsons Paradox Explained

By Jim Frost Leave a Comment

What is Simpsons Paradox?

Simpsons Paradox is a statistical phenomenon that occurs when you combine subgroups into one group. The process of aggregating data can cause the apparent direction and strength of the relationship between two variables to change. [Read more…] about Simpsons Paradox Explained

Filed Under: Basics Tagged With: bias sources, conceptual

Sampling Bias: Definition & Examples

By Jim Frost 2 Comments

What is Sampling Bias?

Sampling bias in statistics occurs when a sample does not accurately represent the characteristics of the population from which it was drawn. When this bias occurs, sample attributes are systematically different from the actual population values. Hence, sampling bias produces a distorted view of the population. Sampling bias often involves human subjects, but it can also apply to samples of objects and animals. Medical researchers refer to this problem as ascertainment bias. [Read more…] about Sampling Bias: Definition & Examples

Filed Under: Basics Tagged With: bias sources, conceptual, experimental design, sampling methods

Confounding Variables Can Bias Your Results

By Jim Frost 84 Comments

In research studies, confounding variables influence both the cause and effect that the researchers are assessing. Consequently, if the analysts do not include these confounders in their statistical model, it can exaggerate or mask the real relationship between two other variables. By omitting confounding variables, the statistical procedure is forced to attribute their effects to variables in the model, which biases the estimated effects and confounds the genuine relationship. Statisticians refer to this distortion as omitted variable bias.
[Read more…] about Confounding Variables Can Bias Your Results

Filed Under: Regression Tagged With: assumptions, bias sources, conceptual

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