Making statistics intuitive
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
A randomized controlled trial (RCT) is a prospective experimental design that randomly assigns participants to an experimental or control group. RCTs are the gold standard for establishing causal relationships and ruling out confounding variables and selection bias. Researchers must be able to control who receives the treatments and who are the controls to use this design. [Read more…] about Randomized Controlled Trial (RCT) Overview
A prospective study is an experimental design that looks forward in time and observes events as they happen. Participants begin the study without having a condition of interest. Then researchers gather data and take measurements at regular intervals to identify the occurrence of specific outcomes along with other data that might relate to them. [Read more…] about Prospective Study: Definition, Benefits & Examples
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
Pearson’s correlation coefficient formula produces a number ranging from -1 to +1, quantifying the strength and direction of a relationship between two continuous variables. A correlation of -1 means a perfect negative relationship, +1 represents a perfect positive relationship, and 0 indicates no relationship. [Read more…] about Correlation Coefficient Formula Walkthrough
A two-way table displays frequencies for combinations of two categorical variables. Columns correspond to the values of one variable, while the rows relate to the other. The intersection of each row and column displays a frequency or relative frequency of observations having a pair of categorical attributes. Statisticians also refer to them as contingency tables. [Read more…] about Two-Way Table Explained
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 range rule of thumb allows you to estimate the standard deviation of a dataset quickly. This process is not as accurate as the actual calculation for the standard deviation, but it’s so simple you can do it in your head. [Read more…] about Range Rule of Thumb: Overview and Formula
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
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
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:
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
Probability sampling is the process of selecting a sample using random sampling. When you use this method, each individual or unit in a population has a known, non-zero chance of being randomly selected for the sample. Statisticians consider this method the most reliable because it tends to minimize sampling bias and produce samples that accurately represent the entire population. A representative sample allows you to use the sample to make inferences about the population. [Read more…] about Probability Sampling Overview
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