• Skip to secondary menu
  • Skip to main content
  • Skip to primary sidebar
  • My Store
  • Glossary
  • Home
  • About Me
  • Contact Me

Statistics By Jim

Making statistics intuitive

  • Graphs
  • Basics
  • Hypothesis Testing
  • Regression
  • ANOVA
  • Probability
  • Time Series
  • Fun
  • Calculators

conceptual

F-table

By Jim Frost 2 Comments

These F-tables provide the critical values for right-tail F-tests. Your F-test results are statistically significant when its test statistic is greater than this value. [Read more…] about F-table

Filed Under: Hypothesis Testing Tagged With: conceptual, distributions, graphs

Sampling Distribution: Definition, Formula & Examples

By Jim Frost 8 Comments

What is a Sampling Distribution?

A sampling distribution of a statistic is a type of probability distribution created by drawing many random samples of a given size from the same population. These distributions help you understand how a sample statistic varies from sample to sample. [Read more…] about Sampling Distribution: Definition, Formula & Examples

Filed Under: Hypothesis Testing Tagged With: conceptual, distributions, graphs

Critical Value: Definition, Finding & Calculator

By Jim Frost 2 Comments

What is a Critical Value?

A critical value defines regions in the sampling distribution of a test statistic. These values play a role in both hypothesis tests and confidence intervals. In hypothesis tests, critical values determine whether the results are statistically significant. For confidence intervals, they help calculate the upper and lower limits. [Read more…] about Critical Value: Definition, Finding & Calculator

Filed Under: Hypothesis Testing Tagged With: conceptual, distributions, graphs

Test Statistic: Definition, Types & Formulas

By Jim Frost 11 Comments

What is a Test Statistic?

A test statistic assesses how consistent your sample data are with the null hypothesis in a hypothesis test. Test statistic calculations take your sample data and boil them down to a single number that quantifies how much your sample diverges from the null hypothesis. As a test statistic value becomes more extreme, it indicates larger differences between your sample data and the null hypothesis. [Read more…] about Test Statistic: Definition, Types & Formulas

Filed Under: Hypothesis Testing Tagged With: conceptual, interpreting results

Reliability vs Validity: Differences & Examples

By Jim Frost 1 Comment

Reliability and validity are criteria by which researchers assess measurement quality. Measuring a person or item involves assigning scores to represent an attribute. This process creates the data that we analyze. However, to provide meaningful research results, that data must be good. And not all data are good! [Read more…] about Reliability vs Validity: Differences & Examples

Filed Under: Basics Tagged With: conceptual

Nominal, Ordinal, Interval, and Ratio Scales

By Jim Frost 17 Comments

The nominal, ordinal, interval, and ratio scales are levels of measurement in statistics. These scales are broad classifications describing the type of information recorded within the values of your variables. Variables take on different values in your data set. For example, you can measure height, gender, and class ranking. Each of these variables uses a distinct level of measurement. [Read more…] about Nominal, Ordinal, Interval, and Ratio Scales

Filed Under: Basics Tagged With: conceptual, data types

Odds Ratio: Formula, Calculating & Interpreting

By Jim Frost 12 Comments

What is an Odds Ratio?

An odds ratio (OR) calculates the relationship between a variable and the likelihood of an event occurring. A common interpretation for odds ratios is identifying risk factors by assessing the relationship between exposure to a risk factor and a medical outcome. For example, is there an association between exposure to a chemical and a disease? [Read more…] about Odds Ratio: Formula, Calculating & Interpreting

Filed Under: Probability Tagged With: conceptual, interpreting results, risk

Case Control Study: Definition, Benefits & Examples

By Jim Frost 2 Comments

What is a Case Control Study?

A case control study is a retrospective, observational study that compares two existing groups. Researchers form these groups based on the existence of a condition in the case group and the lack of that condition in the control group. They evaluate the differences in the histories between these two groups looking for factors that might cause a disease. Case-control studies rank in the middle of the level of evidence hierarchy, offering important retrospective comparisons. [Read more…] about Case Control Study: Definition, Benefits & Examples

Filed Under: Basics Tagged With: conceptual, experimental design, interpreting results

Simple Random Sampling: Definition & Examples

By Jim Frost 1 Comment

What is Simple Random Sampling?

Simple random sampling (SRS) is a probability sampling method where researchers randomly choose participants from a population. All population members have an equal probability of being selected. This method tends to produce representative, unbiased samples. [Read more…] about Simple Random Sampling: Definition & Examples

Filed Under: Basics Tagged With: conceptual, experimental design, sampling methods

Convenience Sampling: Definition & Examples

By Jim Frost 5 Comments

What is Convenience Sampling?

Convenience sampling is a non-probability sampling method where researchers use subjects who are easy to contact and obtain their participation. Researchers find participants in the most accessible places, and they impose no inclusion requirements. Convenience sampling is also known as opportunity or availability sampling. [Read more…] about Convenience Sampling: Definition & Examples

Filed Under: Basics Tagged With: conceptual, experimental design, sampling methods

Systematic Sampling: Definition, Advantages & Examples

By Jim Frost Leave a Comment

What is Systematic Sampling?

Systematic sampling is a probability sampling method for obtaining a representative sample from a population. To use this method, researchers start at a random point and then select subjects at regular intervals of every nth member of the population. Like other probability sampling methods, the researchers must identify their population of interest before sampling from it. This technique is a probability sampling method. [Read more…] about Systematic Sampling: Definition, Advantages & Examples

Filed Under: Basics Tagged With: conceptual, experimental design, sampling methods

Variance: Definition, Formulas & Calculations

By Jim Frost 2 Comments

Variance is a measure of variability in statistics. It assesses the average squared difference between data values and the mean. Unlike some other statistical measures of variability, it incorporates all data points in its calculations by contrasting each value to the mean. [Read more…] about Variance: Definition, Formulas & Calculations

Filed Under: Basics Tagged With: conceptual, distributions, interpreting results

Mean Squared Error (MSE)

By Jim Frost 1 Comment

Mean squared error (MSE) measures the amount of error in statistical models. It assesses the average squared difference between the observed and predicted values. When a model has no error, the MSE equals zero. As model error increases, its value increases. The mean squared error is also known as the mean squared deviation (MSD). [Read more…] about Mean Squared Error (MSE)

Filed Under: Regression Tagged With: conceptual, interpreting results

Validity in Research and Psychology: Types & Examples

By Jim Frost 3 Comments

What is Validity in Psychology, Research, and Statistics?

Validity in research, statistics, psychology, and testing evaluates how well test scores reflect what they’re supposed to measure. Does the instrument measure what it claims to measure? Do the measurements reflect the underlying reality? Or do they quantify something else? [Read more…] about Validity in Research and Psychology: Types & Examples

Filed Under: Basics Tagged With: conceptual, experimental design

Internal and External Validity

By Jim Frost Leave a Comment

Internal and external validity relate to the findings of studies and experiments. [Read more…] about Internal and External Validity

Filed Under: Basics Tagged With: conceptual, experimental design

Uniform Distribution: Definition & Examples

By Jim Frost 4 Comments

What is a Uniform Distribution?

The uniform distribution is a symmetric probability distribution where all outcomes have an equal likelihood of occurring. All values in the distribution have a constant probability, making them uniformly distributed. This distribution is also known as the rectangular distribution because of its shape in probability distribution plots, as I’ll show you below. [Read more…] about Uniform Distribution: Definition & Examples

Filed Under: Probability Tagged With: conceptual, distributions, graphs

Frequency Table: How to Make & Examples

By Jim Frost 2 Comments

What is a Frequency Table?

A frequency table lists a set of values and how often each one appears. Frequency is the number of times a specific data value occurs in your dataset. These tables help you understand which data values are common and which are rare. These tables organize your data and are an effective way to present the results to others. Frequency tables are also known as frequency distributions because they allow you to understand the distribution of values in your dataset. [Read more…] about Frequency Table: How to Make & Examples

Filed Under: Basics Tagged With: conceptual, distributions

Mean Absolute Deviation: Definition, Finding & Formula

By Jim Frost 4 Comments

What is the Mean Absolute Deviation?

The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation. [Read more…] about Mean Absolute Deviation: Definition, Finding & Formula

Filed Under: Basics Tagged With: choosing analysis, conceptual, distributions

Conditional Probability: Definition, Formula & Examples

By Jim Frost 4 Comments

What is Conditional Probability?

A conditional probability is the likelihood of an event occurring given that another event has already happened. Conditional probabilities allow you to evaluate how prior information affects probabilities. For example, what is the probability of A given B has occurred? When you incorporate existing facts into the calculations, it can change the likelihood of an outcome. [Read more…] about Conditional Probability: Definition, Formula & Examples

Filed Under: Probability Tagged With: analysis example, conceptual

Cluster Sampling: Definition, Advantages & Examples

By Jim Frost 1 Comment

What is Cluster Sampling?

Cluster sampling is a method of obtaining a representative sample from a population that researchers have divided into groups. An individual cluster is a subgroup that mirrors the diversity of the whole population while the set of clusters are similar to each other. Typically, researchers use this approach when studying large, geographically dispersed populations because it is a cost-controlling measure. This technique is a probability sampling method. [Read more…] about Cluster Sampling: Definition, Advantages & Examples

Filed Under: Basics Tagged With: conceptual, experimental design, sampling methods

  • « Go to Previous Page
  • Page 1
  • Interim pages omitted …
  • Page 5
  • Page 6
  • Page 7
  • Page 8
  • Page 9
  • Interim pages omitted …
  • Page 13
  • Go to Next Page »

Primary Sidebar

Meet Jim

I’ll help you intuitively understand statistics by focusing on concepts and using plain English so you can concentrate on understanding your results.

Read More...

Buy My Introduction to Statistics Book!

Cover of my Introduction to Statistics: An Intuitive Guide ebook.

Buy My Hypothesis Testing Book!

Cover image of my Hypothesis Testing: An Intuitive Guide ebook.

Buy My Regression Book!

Cover for my ebook, Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models.

Subscribe by Email

Enter your email address to receive notifications of new posts by email.

    I won't send you spam. Unsubscribe at any time.

    Buy My Thinking Analytically Book!

    Cover for my book, Thinking Analytically: An Guide for Making Data-Driven Decisions.

    Top Posts

    • F-table
    • Cronbach’s Alpha: Definition, Calculations & Example
    • Z-table
    • How To Interpret R-squared in Regression Analysis
    • Multicollinearity in Regression Analysis: Problems, Detection, and Solutions
    • How to Interpret P-values and Coefficients in Regression Analysis
    • T-Distribution Table of Critical Values
    • Cohens D: Definition, Using & Examples
    • Interpreting Correlation Coefficients
    • Box Plot Explained with Examples

    Recent Posts

    • Data Collection Methods: Step-By-Step Guide with Examples
    • ANOVA Calculator
    • Positive Predictive Value: Meaning, Formula, and Interpretation
    • Median Absolute Deviation Calculator
    • Median Absolute Deviation: Definition, Finding & Formula
    • Outlier Calculator

    Recent Comments

    • Skata na fas on Comparing Regression Lines with Hypothesis Tests
    • Jim Frost on Comparing Regression Lines with Hypothesis Tests
    • Skata na fas on Comparing Regression Lines with Hypothesis Tests
    • Skata na fas on Comparing Regression Lines with Hypothesis Tests
    • Jim Frost on Pareto Chart: Making, Reading & Examples

    Copyright © 2026 · Jim Frost · Privacy Policy