Making statistics intuitive
Logistic regression statistically models the probabilities of categorical outcomes, which can be binary (two possible values) or have more than two categories. These models use a linear combination of independent variables to help you understand how they correlate with the likelihood of the outcomes and predict them based on specific conditions you enter into the model. [Read more…] about Logistic Regression Overview with Example
Poisson regression statistically models events that you count within a specified observation space. Frequently, analysts define the observation space using time, but it can also relate to a volume, area, or item. These models allow you to understand the independent variables that affect the counts and predict them given specific conditions you enter into the model. [Read more…] about Poisson Regression Analysis Overview with Example
Statistical analysis involves assessing quantitative data to identify data characteristics, trends, and relationships. Scrolling through the raw values in a dataset provides virtually no useful information. Statistical analysis takes the raw data and provides insights into what the data mean. This process can improve understanding of the subject area by testing hypotheses, producing actionable results leading to improved outcomes, and making predictions, amongst many others. [Read more…] about Statistical Analysis Overview
Hypothesis testing in statistics uses sample data to infer the properties of a whole population. These tests determine whether a random sample provides sufficient evidence to conclude an effect or relationship exists in the population. Researchers use them to help separate genuine population-level effects from false effects that random chance can create in samples. These methods are also known as significance testing. [Read more…] about Hypothesis Testing: Uses, Steps & Example
A negative correlation exists when two variables change in opposing directions—as one variable increases, the other decreases. Statisticians also refer to them as an inverse correlation or relationship. This type of correlation has a negative coefficient. [Read more…] about Negative Correlation: Examples & Insights
Sample size is the number of observations or data points collected in a study. It is a crucial element in any statistical analysis because it is the foundation for drawing inferences and conclusions about a larger population. [Read more…] about Sample Size Essentials: The Foundation of Reliable Statistics
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. However, the issues go beyond merely reducing the sample size. In some cases, they can skew your results. [Read more…] about Missing Data Overview: Types, Implications & Handling
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. [Read more…] about Data Aggregation: Strengths & Weaknesses of Aggregated Data
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. [Read more…] about Prospect Theory Overview & Examples
Gage R&R assesses the amount and sources of measurement variation in a measurement system. It evaluates a measurement system’s precision and helps you target improvement efforts where they’re most needed. It does not assess accuracy or bias. [Read more…] about Gage R&R Overview & Example
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 concept applies only to random variation in a process or system and does not pertain to interventions or events that affect the outcome. [Read more…] about Regression to the Mean: Definition & Examples
Self selection bias can occur when individuals choose to participate in a study, survey, or experiment. The bias exists when volunteers have different characteristics than those who do not participate. It is a form of sampling bias stemming from using a nonprobability sampling method, such as volunteer or convenience sampling. [Read more…] about Self Selection Bias Overview & Examples
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. [Read more…] about Attrition Bias: Definition & Examples
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 version of that outcome. [Read more…] about Conjunction Fallacy: Definition & Example
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. [Read more…] about Base Rate Fallacy Overview & Examples
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
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. [Read more…] about Residual Sum of Squares (RSS) Explained
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
What’s the risk? People discuss risk frequently, but it’s not always clearly understood. It is your exposure to danger or adverse outcomes. Statistically, we define risk as the probability of a negative outcome occurring, and there are several ways to calculate it. [Read more…] about Risk Calculations: Relative vs Absolute & Risk Reduction
Omitted variable bias (OVB) occurs when a regression model excludes a relevant variable. The absence of these critical variables can skew the estimated relationships between variables in the model, potentially leading to erroneous interpretations. This bias can exaggerate, mask, or entirely flip the direction of the estimated relationship between an independent and dependent variable. [Read more…] about Omitted Variable Bias: Definition, Avoiding & Example