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Statistics By Jim

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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

Beta Distribution: Uses, Parameters & Examples

By Jim Frost 4 Comments

The beta distribution is a continuous probability distribution that models random variables with values falling inside a finite interval. Use it to model subject areas with both an upper and lower bound for possible values. Analysts commonly use it to model the time to complete a task, the distribution of order statistics, and the prior distribution for binomial proportions in Bayesian analysis. [Read more…] about Beta Distribution: Uses, Parameters & Examples

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

Geometric Distribution: Uses, Calculator & Formula

By Jim Frost Leave a Comment

What is a Geometric Distribution?

The geometric distribution is a discrete probability distribution that calculates the probability of the first success occurring during a specific trial. In other words, during a series of attempts, what is the probability of success first occurring during each attempt? Use this distribution when you need to understand how many attempts are necessary to produce the first successful outcome. [Read more…] about Geometric Distribution: Uses, Calculator & Formula

Filed Under: Probability Tagged With: distributions, graphs

What is Power in Statistics?

By Jim Frost 1 Comment

Power in statistics is the probability that a hypothesis test can detect an effect in a sample when it exists in the population. It is the sensitivity of a hypothesis test. When an effect exists in the population, how likely is the test to detect it in your sample? [Read more…] about What is Power in Statistics?

Filed Under: Hypothesis Testing Tagged With: conceptual

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

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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

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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

Permutation vs Combination: Differences & Examples

By Jim Frost 3 Comments

In mathematics and statistics, permutations vs combinations are two different ways to take a set of items or options and create subsets. For example, if you have ten people, how many subsets of three can you make? While permutation and combination seem like synonyms in everyday language, they have distinct definitions mathematically.

  • Permutations: The order of outcomes matters.
  • Combinations: The order does not matter.

Let’s understand this difference between permutation vs combination in greater detail. And then you’ll learn how to calculate the total number of each. [Read more…] about Permutation vs Combination: Differences & Examples

Filed Under: Probability Tagged With: conceptual

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

Chi-Square Goodness of Fit Test: Uses & Examples

By Jim Frost 4 Comments

The chi-square goodness of fit test evaluates whether proportions of categorical or discrete outcomes in a sample follow a population distribution with hypothesized proportions. In other words, when you draw a random sample, do the observed proportions follow the values that theory suggests. [Read more…] about Chi-Square Goodness of Fit Test: Uses & Examples

Filed Under: Hypothesis Testing Tagged With: analysis example, conceptual, distributions, interpreting results

Sampling Error: Definition, Sources & Minimizing

By Jim Frost 6 Comments

What is Sampling Error?

Sampling error is the difference between a sample statistic and the population parameter it estimates. It is a crucial consideration in inferential statistics where you use a sample to estimate the properties of an entire population. [Read more…] about Sampling Error: Definition, Sources & Minimizing

Filed Under: Hypothesis Testing Tagged With: conceptual

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

Inter-Rater Reliability: Definition, Examples & Assessing

By Jim Frost Leave a Comment

What is Inter-Rater Reliability?

Inter-rater reliability measures the agreement between subjective ratings by multiple raters, inspectors, judges, or appraisers. It answers the question, is the rating system consistent? High inter-rater reliability indicates that multiple raters’ ratings for the same item are consistent. Conversely, low reliability means they are inconsistent. [Read more…] about Inter-Rater Reliability: Definition, Examples & Assessing

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

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

Margin of Error: Formula and Interpreting

By Jim Frost Leave a Comment

What is the Margin of Error?

The margin of error (MOE) for a survey tells you how near you can expect the survey results to be to the correct population value. For example, a survey indicates that 72% of respondents favor Brand A over Brand B with a 3% margin of error. In this case, the actual population percentage that prefers Brand A likely falls within the range of 72% ± 3%, or 69 – 75%. [Read more…] about Margin of Error: Formula and Interpreting

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

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

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