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Related Concept Videos

Significance Testing: Overview01:04

Significance Testing: Overview

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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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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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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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Decision Making: Traditional Method01:14

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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.
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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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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
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Related Experiment Video

Updated: Jun 6, 2025

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Trends in null hypothesis significance testing: Still going strong.

Frank Emmert-Streib1

  • 1Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.

Heliyon
|November 25, 2024
PubMed
Summary
This summary is machine-generated.

Null hypothesis significance testing (NHST) remains popular despite criticisms. A scientometric analysis reveals its continued use in general research, but publication trends vary significantly across specific subject areas and journals, indicating a complex community response.

Keywords:
Change-point analysisNull hypothesis significance testingStatistical inferenceTrends

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Area of Science:

  • Statistics and Scientific Methodology
  • Bibliometrics and Scientometrics

Background:

  • Null hypothesis significance testing (NHST) is a widely used data analysis method.
  • NHST faces criticism due to misuse, misconceptions, and its contribution to the replication crisis.
  • Calls for reform from organizations like the American Statistical Association (ASA) aim to address these issues.

Purpose of the Study:

  • To investigate the scientific community's reaction to discussions surrounding NHST.
  • To analyze publication behavior concerning the usage of NHST through scientometric methods.

Main Methods:

  • Scientometric analysis of bibliographic records.
  • Trend analysis of NHST publication across general community, subject areas, and journals.
  • Change-point analysis to detect shifts in publication behavior.

Main Results:

  • NHST demonstrates increasing popularity in the general scientific community.
  • Significant heterogeneity in publication behavior regarding NHST is observed across different subject areas and journals.
  • No uniform trend in NHST adoption or rejection is evident across all scientific fields.

Conclusions:

  • While NHST remains prevalent overall, its usage patterns are not consistent across the scientific landscape.
  • The community's response to NHST reform calls is varied, with some areas showing continued reliance and others exhibiting different publication trends.