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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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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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Types of Hypothesis Testing01:11

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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 Significance01:50

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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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.
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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Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
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Hypothesis-Testing Demands Trustworthy Data-A Simulation Approach to Inferential Statistics Advocating the Research

Antonia Krefeld-Schwalb1, Erich H Witte2, Frank Zenker3

  • 1Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland.

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|May 10, 2018
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Summary
This summary is machine-generated.

Null-hypothesis significance testing (NHST) struggles with replication due to low statistical power. The proposed research program strategy (RPS) integrates Frequentist and Bayesian methods, offering lower error rates and a solution to the replicability crisis.

Keywords:
Bayes' theoremWald criterioninferential statisticslikelihoodreplicationresearch program strategyt-test

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Area of Science:

  • Psychological research methodology
  • Statistical inference

Background:

  • Null-hypothesis significance testing (NHST) is the primary statistical strategy in psychology.
  • Recent replication failures indicate NHST results often lack statistical power and have high error rates.

Purpose of the Study:

  • To propose the research program strategy (RPS) as a superior alternative to NHST.
  • To address key deficits in both Frequentist and Bayesian inference methods.
  • To provide a tool for restoring trust in psychological research.

Main Methods:

  • Data-simulation was used to estimate error rates of NHST results.
  • The research program strategy (RPS) was developed, integrating Frequentist and Bayesian inference elements.
  • RPS guides from preliminary discovery (H0) to statistical verification (H1).

Main Results:

  • RPS demonstrates significantly lower error rates compared to NHST.
  • RPS effectively aggregates underpowered research findings.
  • RPS addresses limitations of pure Frequentist and standard Bayesian approaches.

Conclusions:

  • The research program strategy (RPS) offers a more robust methodology for empirical research.
  • RPS can help mitigate the impact of the ongoing replicability crisis in psychology.
  • Adopting RPS can enhance the reliability and trustworthiness of scientific findings.