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Related Concept Videos

Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
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Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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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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Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

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A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
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Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

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A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

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Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Null regions: a unified conceptual framework for statistical inference.

Adam H Smiley1,2, Jessica J Glazier1,3, Yuichi Shoda1

  • 1Department of Psychology, University of Washington, Seattle, WA 98195, USA.

Royal Society Open Science
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

Null hypothesis significance testing (NHST) is limited. A new unified framework simplifies alternative tests, enabling researchers to assess findings beyond just

Keywords:
clinical and practical significanceequivalence testingminimum-effect testingnon-inferiority testingopen sciencestrong form hypothesis testing

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Area of Science:

  • Statistics
  • Scientific Methodology
  • Research Design

Background:

  • Traditional null hypothesis significance testing (NHST) is a common statistical method but has limitations.
  • NHST's sole inference is ruling out 'no effect,' which is often insufficient for scientific inquiry.
  • Reliance on NHST complicates replication and theory falsification, especially with increased data precision.

Purpose of the Study:

  • To propose a simple, unified framework for understanding and applying various alternatives to NHST.
  • To provide a single conceptual model that integrates diverse statistical tests used in different scientific fields.
  • To offer a practical approach for researchers to select appropriate statistical tests beyond simple null hypothesis rejection.

Main Methods:

  • Introduced a unified conceptual framework for statistical testing.
  • Proposed a single guiding question for conducting various NHST alternative tests: 'Is the confidence interval entirely outside the null region(s)?'
  • Demonstrated the framework's applicability across different scientific disciplines and testing methodologies.

Main Results:

  • The unified framework simplifies the understanding and application of multiple NHST alternatives.
  • The proposed question provides a consistent method for researchers to perform these advanced statistical tests.
  • The framework facilitates better selection of statistical tests tailored to specific research questions.

Conclusions:

  • A unified framework offers a more effective approach to statistical inference than traditional NHST.
  • This framework enhances the ability of researchers to conduct meaningful data analysis and theory testing.
  • The proposed method aids in choosing the most appropriate statistical test when 'no effect' is not the primary research question.