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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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Errors In Hypothesis Tests01:14

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When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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Statistical Hypothesis Testing01:16

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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Accuracy and Errors in Hypothesis Testing01:13

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
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What is a Hypothesis?01:14

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A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague...
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A Simple Method for Teaching Bayesian Hypothesis Testing in the Brain and Behavioral Sciences.

Thomas J Faulkenberry1

  • 1Department of Psychological Sciences, Tarleton State University, Stephenville, TX 76402.

Journal of Undergraduate Neuroscience Education : JUNE : a Publication of FUN, Faculty for Undergraduate Neuroscience
|July 31, 2018
PubMed
Summary
This summary is machine-generated.

Bayesian inference, specifically the Bayes factor, offers a clear measure of evidence in statistics. This study provides a simple formula for calculating Bayes factors for ANOVA and t-tests, aiding statistics education.

Keywords:
Bayes factorBayesian methodsHypothesis testingStatistical inferencestatistics education

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Area of Science:

  • Brain and behavioral sciences statistics
  • Quantitative methods in psychology and neuroscience

Background:

  • Traditional null hypothesis significance testing (NHST) and p-values are prevalent in undergraduate statistics.
  • Journals in neuroscience and psychology are increasingly adopting alternative statistical methods.
  • Bayesian inference, particularly the Bayes factor, is emerging as a favored alternative for hypothesis testing.

Purpose of the Study:

  • To present a user-friendly formula for computing Bayes factors.
  • To simplify the application of Bayesian statistics in research and teaching.
  • To facilitate the integration of Bayes factors into undergraduate statistics curricula.

Main Methods:

  • Development of a straightforward formula for Bayes factor computation.
  • Application of the formula to two common statistical tests: one-way ANOVA and independent samples t-test.
  • Illustrative examples and reporting recommendations provided.

Main Results:

  • An accessible method for calculating Bayes factors for one-way ANOVA and independent samples t-tests is presented.
  • The proposed formula simplifies the interpretation of evidence supporting one hypothesis over another.
  • Practical guidance is offered for reporting Bayesian analysis results.

Conclusions:

  • The presented formula demystifies Bayes factor computation, making Bayesian statistics more accessible.
  • This approach can enhance the teaching of quantitative methods in brain and behavioral sciences.
  • Adoption of Bayes factors can improve the clarity and interpretability of statistical evidence in research.