## What is a Parsimonious Model?

A parsimonious model in statistics is one that uses relatively few independent variables to obtain a good fit to the data. [Read more…] about What is a Parsimonious Model? Benefits and Selecting

Making statistics intuitive

A parsimonious model in statistics is one that uses relatively few independent variables to obtain a good fit to the data. [Read more…] about What is a Parsimonious Model? Benefits and Selecting

The placebo effect occurs when a fake medical treatment produces real medical benefits psychosomatically. In short, believing in the treatment and the power of the mind can help someone feel better. The placebo effect can be so powerful that it mimics genuine medicine. Consequently, scientists need to control for it when conducting clinical trials. [Read more…] about Placebo Effect Overview: Definition & Examples

P hacking is a set of statistical decisions and methodology choices during research that artificially produces statistically significant results. These decisions increase the probability of false positives—where the study indicates an effect exists when it actually does not. P-hacking is also known as data dredging, data fishing, and data snooping. [Read more…] about What is P Hacking: Methods & Best Practices

The Likert scale is a well-loved tool in the realm of survey research. Named after psychologist Rensis Likert, it measures attitudes or feelings towards a topic on a continuum, typically from one extreme to the other. The scale provides quantitative data about qualitative aspects, such as attitudes, satisfaction, agreement, or likelihood. [Read more…] about Likert Scale: Survey Use & Examples

The Bonferroni correction adjusts your significance level to control the overall probability of a Type I error (false positive) for multiple hypothesis tests. [Read more…] about What is the Bonferroni Correction and How to Use It

The sum of squares (SS) is a statistic that measures the variability of a dataset’s observations around the mean. It’s the cumulative total of each data point’s squared difference from the mean. [Read more…] about Sum of Squares: Definition, Formula & Types

Covariance in statistics measures the extent to which two variables vary linearly. It reveals whether two variables move in the same or opposite directions. [Read more…] about Covariance: Definition, Formula & 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 trimmed mean is a statistical measure that calculates a dataset’s average after removing a certain percentage of extreme values from both ends of the distribution. By excluding outliers, this statistic can provide a more accurate representation of a dataset’s typical or central values. Usually, you’ll trim a percentage of values, such as 10% or 20%. [Read more…] about Trimmed Mean: Definition, Calculating & Benefits

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 root mean square error (RMSE) measures the average difference between a statistical model’s predicted values and the actual values. Mathematically, it is the standard deviation of the residuals. Residuals represent the distance between the regression line and the data points. [Read more…] about Root Mean Square Error (RMSE)

A unimodal distribution in statistics refers to a frequency distribution that has only one peak. Unimodality means that a single value in the distribution occurs more frequently than any other value. The peak represents the most common value, also known as the mode. [Read more…] about Unimodal Distribution Definition & 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

Joint probability is the likelihood that two or more events will coincide. Knowing how to calculate them allows you to solve problems such as the following. What is the probability of:

- Getting two heads in two coin tosses?
- Consecutively drawing two aces from a deck of cards?
- The next customer being a woman who buys a Mac computer?
- A bike rental customer getting both a flat front tire and a flat rear tire?

[Read more…] about Joint Probability: Definition, Formula & Examples

Ecological validity refers to how accurately researchers can generalize a study’s findings to real-world situations. Simply put, it measures how closely an experiment reflects the behaviors and experiences of individuals in their natural environment. [Read more…] about Ecological Validity: Definition & Why It Matters

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

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

Independent events in statistics are those in which one event does not affect the next event. More specifically, the occurrence of one event does not affect the probability of the following event happening. [Read more…] about Independent Events: Definition & Probability

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

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