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Related Concept Videos

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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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.
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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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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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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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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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Why Hypothesis Testers Should Spend Less Time Testing Hypotheses.

Anne M Scheel1, Leonid Tiokhin1, Peder M Isager1

  • 1Human-Technology Interaction Group, Eindhoven University of Technology.

Perspectives on Psychological Science : a Journal of the Association for Psychological Science
|December 16, 2020
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Summary
This summary is machine-generated.

Psychology reforms focused on hypothesis testing, but researchers need better theory development first. Prioritizing nonconfirmatory research builds essential inputs for robust, testable scientific theories.

Keywords:
exploratory researchhypothesis testingreplication crisis

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Area of Science:

  • Psychology
  • Social Sciences
  • Scientific Methodology

Background:

  • The mindless use of null-hypothesis significance tests has rendered social science research on theories largely uninterpretable.
  • Recent reforms in psychology have emphasized formalizing hypothesis testing procedures in response to the replication crisis.

Purpose of the Study:

  • To argue that premature hypothesis testing hinders scientific progress.
  • To advocate for a shift towards nonconfirmatory research activities to strengthen psychological theories.

Main Methods:

  • Conceptual analysis of current psychological research practices.
  • Discussion of Paul Meehl's critiques of null-hypothesis significance testing.
  • Proposal for prioritizing theory-building activities before hypothesis testing.

Main Results:

  • Current reforms risk forcing premature hypothesis testing, which is counterproductive.
  • Nonconfirmatory research activities are crucial for developing informative hypothesis tests.
  • Establishing a sound "derivation chain" between theory and test requires foundational work.

Conclusions:

  • Psychological scientists need more time for concept formation, measure development, and establishing relationships between concepts.
  • Incentivizing foundational research inputs is essential for developing strong, testable theories.
  • Shifting focus to nonconfirmatory research can resolve issues in psychology's reform movement and fulfill Meehl's vision.