• 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

Basics

Cronbach’s Alpha: Definition, Calculations & Example

By Jim Frost 4 Comments

What is Cronbach’s Alpha?

Cronbach’s alpha coefficient measures the internal consistency, or reliability, of a set of survey items. Use this statistic to help determine whether a collection of items consistently measures the same characteristic. Cronbach’s alpha quantifies the level of agreement on a standardized 0 to 1 scale. Higher values indicate higher agreement between items. [Read more…] about Cronbach’s Alpha: Definition, Calculations & Example

Filed Under: Basics Tagged With: analysis example, conceptual, interpreting results

Cohens D: Definition, Using & Examples

By Jim Frost 2 Comments

What is Cohens d?

Cohens d is a standardized effect size for measuring the difference between two group means. Frequently, you’ll use it when you’re comparing a treatment to a control group. It can be a suitable effect size to include with t-test and ANOVA results. The field of psychology frequently uses Cohens d. [Read more…] about Cohens D: Definition, Using & Examples

Filed Under: Basics Tagged With: conceptual

Representative Sample: Definition, Uses & Methods

By Jim Frost Leave a Comment

What is a Representative Sample?

A representative sample is one where the individuals in the sample reflect the properties of an entire population. Use a representative sample when you want to generalize the results from the sample to a population. By studying a representative sample, you can approximate the properties of the population from which it was drawn. [Read more…] about Representative Sample: Definition, Uses & Methods

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

Difference Between Standard Deviation and Standard Error

By Jim Frost 6 Comments

The difference between a standard deviation and a standard error can seem murky. Let’s clear that up in this post!

Standard deviation (SD) and standard error (SE) both measure variability. High values of either statistic indicate more dispersion. However, that’s where the similarities end. The standard deviation is not the same as the standard error. [Read more…] about Difference Between Standard Deviation and Standard Error

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

Sampling Methods: Different Types in Research

By Jim Frost 2 Comments

What Are Sampling Methods?

Sampling methods are the processes by which you draw a sample from a population. When performing research, you’re typically interested in the results for an entire population. Unfortunately, they are almost always too large to study fully. Consequently, researchers use samples to draw conclusions about a population—the process of making statistical inferences. [Read more…] about Sampling Methods: Different Types in Research

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

Conditional Distribution: Definition & Finding

By Jim Frost Leave a Comment

What is a Conditional Distribution?

A conditional distribution is a distribution of values for one variable that exists when you specify the values of other variables. This type of distribution allows you to assess the dispersal of your variable of interest under specific conditions, hence the name. [Read more…] about Conditional Distribution: Definition & Finding

Filed Under: Basics Tagged With: conceptual, distributions

Marginal Distribution: Definition & Finding

By Jim Frost Leave a Comment

What is a Marginal Distribution?

A marginal distribution is a distribution of values for one variable that ignores a more extensive set of related variables in a dataset.

That definition sounds a bit convoluted, but the concept is simple. The idea is that when you have a larger set of related variables that you collected for a study, you might want to focus on one of them to answer a specific question. [Read more…] about Marginal Distribution: Definition & Finding

Filed Under: Basics Tagged With: conceptual, distributions

Content Validity: Definition, Examples & Measuring

By Jim Frost Leave a Comment

What is Content Validity?

Content validity is the degree to which a test or assessment instrument evaluates all aspects of the topic, construct, or behavior that it is designed to measure. Do the items fully cover the subject? High content validity indicates that the test fully covers the topic for the target audience. Lower results suggest that the test does not contain relevant facets of the subject matter. [Read more…] about Content Validity: Definition, Examples & Measuring

Filed Under: Basics Tagged With: conceptual

Parameter vs Statistic: Examples & Differences

By Jim Frost 1 Comment

Parameters are numbers that describe the properties of entire populations. Statistics are numbers that describe the properties of samples. [Read more…] about Parameter vs Statistic: Examples & Differences

Filed Under: Basics

Spurious Correlation: Definition, Examples & Detecting

By Jim Frost 5 Comments

What is a Spurious Correlation?

A spurious correlation occurs when two variables are correlated but don’t have a causal relationship. In other words, it appears like values of one variable cause changes in the other variable, but that’s not actually happening. [Read more…] about Spurious Correlation: Definition, Examples & Detecting

Filed Under: Basics Tagged With: conceptual

Contingency Table: Definition, Examples & Interpreting

By Jim Frost Leave a Comment

What is a Contingency Table?

A contingency table displays frequencies for combinations of two categorical variables. Analysts also refer to contingency tables as crosstabulation and two-way tables. [Read more…] about Contingency Table: Definition, Examples & Interpreting

Filed Under: Basics Tagged With: conceptual, distributions

Cumulative Frequency: Finding & Interpreting

By Jim Frost Leave a Comment

What is Cumulative Frequency?

Cumulative frequency is the running total of frequencies in a table. Use cumulative frequencies to answer questions about how often a characteristic occurs above or below a particular value. It is also known as a cumulative frequency distribution.

For example, how many students are in the 4th grade or lower at a school? [Read more…] about Cumulative Frequency: Finding & Interpreting

Filed Under: Basics Tagged With: conceptual, distributions

Cohort Study: Definition, Benefits & Examples

By Jim Frost Leave a Comment

What is a Cohort Study?

A cohort study is a longitudinal experimental design that follows a group of participants who share a defining characteristic. For example, a cohort study can select subjects who have exposure to a risk factor, are in the same profession, population or generation, or experience a particular event, such as a medical procedure. This design determines whether exposure to a risk factor affects an outcome. [Read more…] about Cohort Study: Definition, Benefits & Examples

Filed Under: Basics Tagged With: conceptual, experimental design

How to Find the Mode

By Jim Frost Leave a Comment

There are several ways to find the mode depending upon the data type and sample size. In statistics, the mode is the most frequently occurring value in a data set. It is a measure of central tendency. To learn more about the mode, read my post, Measures of Central Tendency. [Read more…] about How to Find the Mode

Filed Under: Basics Tagged With: conceptual

Bimodal Distribution: Definition, Examples & Analysis

By Jim Frost 1 Comment

A bimodal distribution has two peaks. In the context of a continuous probability distribution, modes are peaks in the distribution. The graph below shows a bimodal distribution. [Read more…] about Bimodal Distribution: Definition, Examples & Analysis

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

Quartile: Definition, Finding, and Using

By Jim Frost Leave a Comment

What are Quartiles?

Quartiles are three values that split your dataset into quarters. [Read more…] about Quartile: Definition, Finding, and Using

Filed Under: Basics Tagged With: conceptual, distributions

Construct Validity: Definition and Assessment

By Jim Frost Leave a Comment

What is Construct Validity?

Construct validity relates to the soundness of inferences that you draw from test scores and other measurements. Specifically, it addresses whether a test measures the intended construct. For example, does a test that evaluates self-esteem truly measure that construct or something else? [Read more…] about Construct Validity: Definition and Assessment

Filed Under: Basics Tagged With: conceptual

Qualitative Research: Goals, Methods & Benefits

By Jim Frost 4 Comments

Qualitative research aims to understand ideas, experiences, and opinions using non-numeric data, such as text, audio, and visual recordings. The focus is on language, behaviors, and social structures. Qualitative researchers want to present personal experiences and produce narrative stories that use natural language to provide meaningful answers to their research questions. [Read more…] about Qualitative Research: Goals, Methods & Benefits

Filed Under: Basics Tagged With: conceptual

What is a Variable?

By Jim Frost Leave a Comment

The definition of a variable changes depending on the context. Typically, a letter represents them, and it stands in for a numerical value. In algebra, a variable represents an unknown value that you need to find. For mathematical functions and equations, you input their values to calculate the output. In an equation, a coefficient is a fixed value by which you multiply the variable.

In statistics, a variable is a characteristic of interest that you measure, record, and analyze. Statisticians understand them by defining the type of information they record and their role in an experiment or study. [Read more…] about What is a Variable?

Filed Under: Basics Tagged With: conceptual

Kurtosis: Definition, Leptokurtic & Platykurtic

By Jim Frost 3 Comments

What is Kurtosis?

Kurtosis is a statistic that measures the extent to which a distribution contains outliers. It assesses the propensity of a distribution to have extreme values within its tails. There are three kinds of kurtosis: leptokurtic, platykurtic, and mesokurtic. Statisticians define these types relative to the normal distribution. Higher kurtosis values indicate that the distribution has more outliers falling relatively far from the mean. Distributions with smaller values have a lower tendency for producing extreme values. When you’re assessing a sample, outliers have the greatest impact on this statistic. [Read more…] about Kurtosis: Definition, Leptokurtic & Platykurtic

Filed Under: Basics Tagged With: conceptual, distributions

  • « Go to Previous Page
  • Go to page 1
  • Go to page 2
  • Go to page 3
  • Go to page 4
  • Interim pages omitted …
  • Go to page 6
  • 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.

    Follow Me

    • FacebookFacebook
    • RSS FeedRSS Feed
    • TwitterTwitter

    Top Posts

    • How to Interpret P-values and Coefficients in Regression Analysis
    • How To Interpret R-squared in Regression Analysis
    • Mean, Median, and Mode: Measures of Central Tendency
    • Measures of Variability: Range, Interquartile Range, Variance, and Standard Deviation
    • Multicollinearity in Regression Analysis: Problems, Detection, and Solutions
    • How to Interpret the F-test of Overall Significance in Regression Analysis
    • One-Tailed and Two-Tailed Hypothesis Tests Explained
    • Choosing the Correct Type of Regression Analysis
    • The Importance of Statistics
    • Z-table

    Recent Posts

    • Principal Component Analysis Guide & Example
    • Fishers Exact Test: Using & Interpreting
    • Percent Change: Formula and Calculation Steps
    • X and Y Axis in Graphs
    • Simpsons Paradox Explained
    • Covariates: Definition & Uses

    Recent Comments

    • Sultan Mahmood on Linear Regression Equation Explained
    • Sanjay Kumar P on What is the Mean and How to Find It: Definition & Formula
    • Dave on Control Variables: Definition, Uses & Examples
    • Jim Frost on How High Does R-squared Need to Be?
    • Mark Solomons on How High Does R-squared Need to Be?

    Copyright © 2023 · Jim Frost · Privacy Policy