Making statistics intuitive
A longitudinal study is an experimental design that takes repeated measurements of the same subjects over time. These studies can span years or even decades. Unlike cross-sectional studies, which analyze data at a single point, longitudinal studies track changes and developments, producing a more dynamic assessment. [Read more…] about Longitudinal Study: Overview, Examples & Benefits
Correlation vs causation in statistics is a critical distinction. And you’ve undoubtedly heard that correlation doesn’t imply causation. Why is that the case, what are the differences between them, and why do they matter? Those are the topics of this post! [Read more…] about Correlation vs Causation: Understanding the Differences
Use one way ANOVA to compare the means of three or more groups. This analysis is an inferential hypothesis test that uses samples to draw conclusions about populations. Specifically, it tells you whether your sample provides sufficient evidence to conclude that the groups’ population means are different. ANOVA stands for analysis of variance. [Read more…] about One Way ANOVA Overview & Example
Observational studies and experiments are two standard research methods for understanding the world. Both research designs collect data and use statistical analysis to understand relationships between variables. Beyond that commonality, they are vastly different and have dissimilar sets of pros and cons. [Read more…] about Observational Study vs Experiment with Examples
Goodness of fit evaluates how well observed data align with the expected values from a statistical model. [Read more…] about Goodness of Fit: Definition & Tests
Use the binomial distribution formula to calculate the likelihood an event will occur a specific number of times in a set number of opportunities. I’ll show you the binomial distribution formula to calculate these probabilities manually.
In this post, I’ll walk you through the formulas for how to find the probability, mean, and standard deviation of the binomial distribution and provide worked examples. [Read more…] about Binomial Distribution Formula: Probability, Standard Deviation & Mean
The expected value in statistics is the long-run average outcome of a random variable based on its possible outcomes and their respective probabilities. Essentially, if an experiment (like a game of chance) were repeated, the expected value tells us the average result we’d see in the long run. Statisticians denote it as E(X), where E is “expected value,” and X is the random variable. [Read more…] about Expected Value: Definition, Formula & Finding
Use a one sample t test to evaluate a population mean using a single sample. Usually, you conduct this hypothesis test to determine whether a population mean differs from a hypothesized value you specify. The hypothesized value can be theoretically important in the study area, a reference value, or a target. [Read more…] about One Sample T Test: Definition, Using & Example
A QQ plot, or Quantile-Quantile plot, is a visual tool that determines whether a sample:
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 Bernoulli distribution is a discrete probability distribution that models a binary outcome for one trial. Use it for a random variable that can take one of two outcomes: success (k = 1) or failure (k = 0), much like a coin toss. Statisticians refer to these trials as Bernoulli trials. [Read more…] about Bernoulli Distribution: Uses, Formula & Example
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
A t test is a statistical hypothesis test that assesses sample means to draw conclusions about population means. Frequently, analysts use a t test to determine whether the population means for two groups are different. For example, it can determine whether the difference between the treatment and control group means is statistically significant. [Read more…] about T Test Overview: How to Use & Examples
The Wilcoxon signed rank test is a nonparametric hypothesis test that can do the following:
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
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