Making statistics intuitive
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
Control charts determine whether a process is stable and in control or whether it is out of control and in need of adjustment. Some degree of variation is inevitable in any process. Control charts help prevent overreactions to normal process variability while prompting quick responses to unusual variation. Control charts are also known as Shewhart charts. [Read more…] about Control Chart: Uses, Example, and Types
Monte Carlo simulation uses random sampling to produce simulated outcomes of a process or system. This method uses random sampling to generate simulated input data and enters them into a mathematical model that describes the system. The simulation produces a distribution of outcomes that analysts can use to derive probabilities. [Read more…] about Monte Carlo Simulation: Make Better Decisions
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
Fishers exact test determines whether a statistically significant association exists between two categorical variables.
For example, does a relationship exist between gender (Male/Female) and voting Yes or No on a referendum? [Read more…] about Fishers Exact Test: Using & Interpreting
Percent change is the relative difference between an old value and a new value. Positive values represent an increase over time, while negative numbers indicate a reduction.
For example, if the price of a candy bar changes from $1 to $1.10, it’s a 10% increase. [Read more…] about Percent Change: Formula and Calculation Steps
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
A weighted average is a type of mean that gives differing importance to the values in a dataset. In contrast, the regular average, or arithmetic mean, gives equal weight to all observations. The weighted average is also known as the weighted mean, and I’ll use those terms interchangeably. [Read more…] about Weighted Average: Formula & Calculation Examples
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
Use a Z test when you need to compare group means. Use the 1-sample analysis to determine whether a population mean is different from a hypothesized value. Or use the 2-sample version to determine whether two population means differ. [Read more…] about Z Test: Uses, Formula & 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
A retrospective study looks back in time and assesses events that have already occurred. The researchers already know the outcome for each subject when the project starts. Instead of recording data going forward as events happen, these studies use participant recollection and data that were previously recorded for reasons not relating to the project. These studies typically don’t follow patients into the future. [Read more…] about Retrospective Study: Definition & Examples
Political polls provide crucial information during elections. When done correctly, small samples can predict the outcomes. We’re approaching election time here in the U.S., and campaigns are switching into high gear along with political polls!
You’d think that conducting a poll is straightforward. You ask a bunch of people who they’ll vote for and count the results, right? But it’s not quite that simple.
For starters, political polls face the same challenges as other polls. However, a poll taken for political reasons also has some unique issues. When pollsters do everything correctly, the small, sampled group accurately reflects the entire population. The trick is to do it correctly!
In this post, learn how statistical concepts apply to political polls and the extra difficulties they face. [Read more…] about Political Polls and Their Challenges
Survivorship bias, or survivor bias, occurs when you tend to assess successful outcomes and disregard failures. This sampling bias paints a rosier picture of reality than is warranted by skewing the mean results upward. [Read more…] about Survivorship Bias: Definition, Examples & Avoiding
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