Making statistics intuitive
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
A random variable is a variable where chance determines its value. They can take on either discrete or continuous values, and understanding the properties of each type is essential in many statistical applications. Random variables are a key concept in statistics and probability theory. [Read more…] about Random Variable: Discrete & Continuous
A sampling frame lists all members of the population you’re studying. Your target population is the general concept of the group you’re assessing, while a sampling frame specifically lists all population members and how to contact them. It might also include demographic information for each person because some methods, such as stratified sampling, require it. [Read more…] about Sampling Frame: 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:
The Fibonacci sequence is a series of numbers that appears in surprisingly many aspects of nature, from the branching of trees to the spiral shapes of shells. This series is named after the Italian mathematician Leonardo Fibonacci. [Read more…] about Fibonacci Sequence: Formula & Uses
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
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
A cumulative distribution function (CDF) describes the probabilities of a random variable having values less than or equal to x. It is a cumulative function because it sums the total likelihood up to that point. Its output always ranges between 0 and 1. [Read more…] about Cumulative Distribution Function (CDF): Uses, Graphs & vs PDF
Population vs sample is a crucial distinction in statistics. Typically, researchers use samples to learn about populations. Let’s explore the differences between these concepts! [Read more…] about Population vs Sample: Uses and Examples
Calculating percentages is a standard mathematical procedure. A percent is a ratio that you write as a fraction of 100. In this article, learn why percentages are crucial summary measures and how to calculate them. [Read more…] about How to Calculate a Percentage
Principal Component Analysis (PCA) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices. These indices retain most of the information in the original set of variables. Analysts refer to these new values as principal components. [Read more…] about Principal Component Analysis Guide & Example
The X and Y axis form the basis of most graphs. These two perpendicular lines define the coordinate plane. X and Y values can specify any point on this plane using the Cartesian coordinate system. [Read more…] about X and Y Axis in Graphs
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
Covariates are continuous independent variables (or predictors) in a regression or ANOVA model. These variables can explain some of the variability in the dependent variable.
That definition of covariates is simple enough. However, the usage of the term has changed over time. Consequently, analysts can have drastically different contexts in mind when discussing covariates. [Read more…] about Covariates: Definition & Uses
Concurrent validity is the degree to which assessment scores correlate with a criterion variable when researchers measure both variables at approximately the same time (i.e., concurrently). This method validates an assessment instrument by comparing its scores to another test or variable that researchers had validated previously. [Read more…] about Concurrent Validity: Definition, Assessing & Examples
Criterion validity (aka criterion related validity) is the degree to which scores from a construct assessment correlate with a manifestation of that construct in the real world (the criterion). [Read more…] about Criterion Validity: Definition, Assessing & Examples
Predictive validity is the degree to which a test score or construct scale predicts a criterion variable measuring a future outcome, behavior, or performance. Evaluating predictive validity involves assessing the correlation between the pre-test score and the subsequent criterion outcome. [Read more…] about Predictive Validity: Definition, Assessing & Examples
Statistical significance is the goal for most researchers analyzing data. But what does statistically significant mean? Why and when is it important to consider? How do P values fit in with statistical significance? I’ll answer all these questions in this blog post!
Evaluate statistical significance when using a sample to estimate an effect in a population. It helps you determine whether your findings are the result of chance versus an actual effect of a variable of interest. [Read more…] about Statistical Significance: Definition & Meaning
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