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What are Descriptive Statistics?

Descriptive statistics summarize the properties of a dataset using summary statistics, tables, and graphs. These descriptions characterize vital information about the variables, their relationships, and trends. Ideally, they provide a clearer picture of the data than the raw values. In short, they describe the essential features of a sample. [Read more…] about Descriptive Statistics Definition and Examples

Controlled Experiment: Definition & Examples

What is a Controlled Experiment?

A controlled experiment assesses causal relationships between treatments and outcomes by systematically manipulating the treatments and controlling other variables. The goal is to determine whether the treatment causes changes in the outcomes. [Read more…] about Controlled Experiment: Definition & Examples

Hawthorne Effect: Definition & Examples

What is the Hawthorne Effect?

The Hawthorne effect occurs when experimental participants change their behavior because they know researchers are watching them. Typically, this effect refers to cases where subjects improve their performance levels. However, these are short-term improvements that vanish when the observation stops. Consequently, the study results are deceptive because they do not reflect a natural response to the experimental factors. [Read more…] about Hawthorne Effect: Definition & Examples

Naturalistic Observation: Definition & Examples

What is Naturalistic Observation?

Naturalistic observation is a research method in psychology and other fields where investigators watch subjects performing natural behaviors in real-world settings. These studies don’t manipulate variables to see how that affects the outcomes. Instead, the focus is on recording normal behaviors in ordinary settings. Typically, the goal is to make observations while minimizing the effect of the researcher’s presence. These studies are also known as field studies. [Read more…] about Naturalistic Observation: Definition & Examples

Imputation of Missing Values Overview

What is Imputation?

Imputation in statistics is the process of replacing missing data points with plausible values. This technique is crucial because missing values can bias the statistical results. When applied correctly, imputed data reduce this bias. [Read more…] about Imputation of Missing Values Overview

Multinomial Logistic Regression: Overview & Example

What is Multinomial Logistic Regression?

Multinomial logistic regression statistically models the probabilities of at least three categorical outcomes that do not have a natural order. This technique uses a linear combination of independent variables to explore correlations with outcome likelihoods and to predict outcomes using specific input conditions. This analysis is also known as nominal logistic regression. [Read more…] about Multinomial Logistic Regression: Overview & Example

Logistic Regression Overview with Example

By Jim Frost 3 Comments

What is Logistic Regression?

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 Analysis Overview with Example

What is Poisson Regression?

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 Overview

What is Statistical Analysis?

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: Uses, Steps & Example

By Jim Frost 8 Comments

What is Hypothesis Testing?

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

Negative Correlation: Examples & Insights

What Does a Negative Correlation Mean?

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 Essentials: The Foundation of Reliable Statistics

By Jim Frost 4 Comments

What is Sample Size?

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 Overview: Types, Implications & Handling

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: Strengths & Weaknesses of Aggregated Data

By Jim Frost 4 Comments

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. [Read more…] about Data Aggregation: Strengths & Weaknesses of Aggregated Data

Prospect Theory Overview & Examples

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. [Read more…] about Prospect Theory Overview & Examples

Gage R&R Overview & Example

What is Gage R&R?

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: Definition & Examples

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 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 Overview & Examples

What is Self Selection Bias?

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: Definition & Examples