Making statistics intuitive
A case study is an in-depth investigation of a single person, group, event, or community. This research method involves intensively analyzing a subject to understand its complexity and context. The richness of a case study comes from its ability to capture detailed, qualitative data that can offer insights into a process or subject matter that other research methods might miss. [Read more…] about What is a Case Study? Definition & Examples
In statistics, the symbols and formulas for basic concepts such as the mean provide a foundational understanding of data analysis. Understanding the mean involves more than just knowing how to calculate an average; it’s about recognizing the nuances that differentiate a population mean from a sample mean. This distinction is crucial in statistical analysis, as the approach and symbol used for each vary (mu vs. x bar). [Read more…] about Sample Mean vs Population Mean: Symbol & Formulas
A correlational study is an experimental design that evaluates only the correlation between variables. The researchers record measurements but do not control or manipulate the variables. Correlational research is a form of observational study. [Read more…] about Correlational Study Overview & Examples
A type 2 error (AKA Type II error) occurs when you fail to reject a false null hypothesis in a hypothesis test. In other words, a statistically non-significant test result indicates that a population effect does not exist when it actually does. A type 2 error is a false negative because the effect exists in the population, but the test doesn’t detect it in the sample. [Read more…] about Type 2 Error Overview & Example
A type 1 error (AKA Type I error) occurs when you reject a true null hypothesis in a hypothesis test. In other words, a statistically significant test result indicates that a population effect exists when it does not. A type 1 error is a false positive because the test detects an effect in the sample that doesn’t exist in the population. [Read more…] about Type 1 Error Overview & Example
A cross-sectional study is an experimental design that analyzes data from a representative sample at a specific point in time. Researchers usually evaluate multiple attributes at once when using this design. Unlike longitudinal studies, these studies don’t track changes over time. [Read more…] about Cross Sectional Study: Overview, Examples & Benefits
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