Missing data refers to the absence of data entries in a dataset where values are expected but not recorded. They’re the blank cells in your data sheet. Missing values for specific variables or participants can occur for many reasons, including incomplete data entry, equipment failures, or lost files. When data are missing, it’s a problem. […] The post Missing Data Overview: Types, Implications & Handling appeared first on Statistics By Jim ..read more

What is Data Aggregation? Data aggregation is a crucial process that involves collecting data and summarizing it in a concise form. This method transforms atomic data rows—sourced from diverse origins—into comprehensive totals or summary statistics. Aggregated data, typically housed in data warehouses, enhances analytical capabilities and significantly speeds up querying large datasets. Data aggregation plays […] The post Data Aggregation: Strengths & Weaknesses of Aggregated Data appeared first on Statistics By Jim ..read more

What is Prospect Theory? Prospect Theory states that individuals place greater weight on losses than gains while making decisions. It is a descriptive model of how individuals make decisions involving risk and uncertainty proposed by Daniel Kahneman and Amos Tversky in 1979. Prospect theory describes how people evaluate and choose between different options. For example, […] The post Prospect Theory Overview & Examples appeared first on Statistics By Jim ..read more

What is Regression to the Mean? Regression to the mean is the statistical tendency for an extreme sample or observed value to be followed by a more average one. It is also known as reverting to the mean, highlighting the propensity for a later observation to move closer to the mean after an extreme value. […] The post Regression to the Mean: Definition & Examples appeared first on Statistics By Jim ..read more

What is Attrition Bias? Attrition bias in research occurs when study participants who drop out have characteristics that differ significantly from those who remain. This selective dropout can lead to skewed results and misinterpretations if the researchers don’t adequately address it. This threat is higher for longitudinal studies and those with relatively high attrition rates. […] The post Attrition Bias: Definition & Examples appeared first on Statistics By Jim ..read more

What is the Conjunction Fallacy? The conjunction fallacy is a cognitive bias that occurs when someone mistakenly believes that two events occurring together are more likely than either of the two events alone. In other words, it’s the mistaken belief that a precisely detailed, multifaced outcome is more likely to occur than a more generalized […] The post Conjunction Fallacy: Definition & Example appeared first on Statistics By Jim ..read more

What is Base Rate Fallacy? Base rate fallacy is a cognitive bias that occurs when a person misjudges an outcome by giving too much weight to case-specific details and overlooks crucial probability information that applies to all cases in a population. That vital probability is the outcome’s base rate of occurrence in the population. In […] The post Base Rate Fallacy Overview & Examples appeared first on Statistics By Jim ..read more

What is a Quasi Experimental Design? 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. In contrast, true experiments […] The post Quasi Experimental Design Overview & Examples appeared first on Statistics By Jim ..read more

The residual sum of squares (RSS) measures the difference between your observed data and the model’s predictions. It is the portion of variability your regression model does not explain, also known as the model’s error. Use RSS to evaluate how well your model fits the data. In least squares regression, the concept of the sum […] The post Residual Sum of Squares (RSS) Explained appeared first on Statistics By Jim ..read more

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 […] The post Covariance vs Correlation: Understanding the Differences appeared first on Statistics By Jim ..read more