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Related Concept Videos

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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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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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.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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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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Significance Testing: Overview01:04

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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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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.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Related Experiment Video

Updated: Oct 16, 2025

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
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Why we habitually engage in null-hypothesis significance testing: A qualitative study.

Jonah Stunt1,2, Leonie van Grootel1,3, Lex Bouter4,5

  • 1Department of Health Sciences, Section of Methodology and Applied Statistics, Vrije Universiteit, Amsterdam, The Netherlands.

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Summary

Null Hypothesis Significance Testing (NHST) persists despite drawbacks due to scientific climate, duty, and reactivity. Overcoming interdependency barriers can reduce reliance on NHST and p-values.

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Area of Science:

  • Statistics
  • Scientific Methodology
  • Research Practices

Background:

  • Null Hypothesis Significance Testing (NHST) is a prevalent statistical method for population effect inference.
  • Despite documented issues and available alternatives, NHST remains widely used.
  • Reasons for NHST's continued dominance are not fully understood.

Purpose of the Study:

  • To investigate perceived barriers and facilitators influencing the use of NHST and alternative statistical procedures.
  • To explore stakeholder perspectives within the scientific system regarding statistical practices.

Main Methods:

  • Conducted semi-structured interviews and focus groups with researchers, statistics lecturers, journal editors, and funding agency program leaders.
  • Employed the constant comparison method for data analysis to thoroughly explore emerging themes.
  • Developed a theory explaining the sustained use of NHST based on identified themes.

Main Results:

  • Identified interrelated facilitators and barriers to adopting alternatives to NHST.
  • Key themes included the scientific climate, perceived scientific duty, and participant reactivity.
  • Participants expressed a sense of dependency and reactivity, awaiting external initiatives, contributing to NHST's standard use.

Conclusions:

  • Perceived barriers create significant challenges for behavioral change, fostering interdependency among stakeholders.
  • Reducing the scientific community's reliance on NHST and p-values requires addressing these systemic issues.
  • Incremental steps can help decrease the dependence on NHST and p-values.