Making statistics intuitive
A quasi experimental design is a method for identifying causal relationships that does not randomly assign participants to the experimental groups. Instead, researchers use a non-random process. For example, they might use an eligibility cutoff score or preexisting groups to determine who receives the treatment. [Read more…] about Quasi Experimental Design Overview & Examples
Covariance vs correlation both evaluate the linear relationship between two continuous variables. While this description makes them sound similar, there are stark differences in how to interpret them.
Although these statistics are closely related, they are distinct concepts. How are they different?
In this post, learn about the differences between covariance vs correlation and what you can learn from each. [Read more…] about Covariance vs Correlation: Understanding the Differences
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. Case reports are near the bottom of the level of evidence ranking, offering descriptive insights from individual or small patient series. [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 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
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
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. It is a type of controlled experiment. Randomized controlled trials are considered one of the highest forms of design in the level of evidence ranking. [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 cross tabulation and contingency tables. [Read more…] about Two-Way Table Explained
Covariance in statistics measures the extent to which two variables vary linearly. The covariance formula reveals whether two variables move in the same or opposite directions. [Read more…] about Covariance: Formula, Definition & 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