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Probability in Statistics01:14

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Binomial Probability Distribution01:15

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Unusual Results01:16

Unusual Results

Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...

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Related Experiment Video

Updated: May 18, 2026

A Within-Subject Experimental Design using an Object Location Task in Rats
09:28

A Within-Subject Experimental Design using an Object Location Task in Rats

Published on: May 6, 2021

Explorations in statistics: permutation methods.

Douglas Curran-Everett1

  • 1Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, Colorado 80206, USA. EverettD@NJHealth.org

Advances in Physiology Education
|September 7, 2012
PubMed
Summary
This summary is machine-generated.

Explore permutation methods for statistical analysis, a hands-on approach to hypothesis testing. These empirical procedures assess experimental results by rearranging data to determine statistical significance without relying solely on theory.

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Area of Science:

  • Statistics
  • Experimental Science

Background:

  • Active exploration enhances understanding in statistics and science.
  • Traditional statistical theory may not always be sufficient for result assessment.

Purpose of the Study:

  • To explore permutation methods as an empirical approach to hypothesis testing.
  • To explain how permutation procedures assess experimental results.

Main Methods:

  • Permutation methods operate directly on experimental data.
  • Procedures involve rearranging observations to assess the extremity of a sample statistic.

Main Results:

  • Permutation procedures provide a P value.
  • The P value represents the proportion of data rearrangements yielding an extreme statistic.

Conclusions:

  • Permutation methods offer a data-driven alternative for hypothesis testing.
  • These methods are valuable when statistical theory alone is insufficient.