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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

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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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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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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Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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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.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines.

Jingyi Jessica Li1, Xin Tong2

  • 1Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA.

Patterns (New York, N.Y.)
|October 19, 2020
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Summary
This summary is machine-generated.

Choosing between hypothesis testing and binary classification is crucial for data analysis. This study clarifies their distinctions and provides guidelines for selecting the right strategy, aiding researchers in making informed data-driven decisions.

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Area of Science:

  • Data Science
  • Statistical Analysis
  • Bioinformatics

Background:

  • Binary decision-making is prevalent in scientific research and industry.
  • Hypothesis testing and binary classification are two distinct yet related data science strategies.
  • Choosing between these methods can be confusing for data analysts.

Purpose of the Study:

  • To clarify the key distinctions between hypothesis testing and binary classification.
  • To provide practical guidelines for data analysts to select the appropriate strategy.
  • To illustrate the application of these guidelines in a real-world scenario.

Main Methods:

  • Comparative analysis of hypothesis testing and binary classification.
  • Development of five practical guidelines for strategy selection.
  • Case study demonstration using cancer driver gene prediction.

Main Results:

  • Key differences between the two strategies are identified across three aspects.
  • A set of five actionable guidelines are proposed for practical application.
  • The guidelines are shown to be effective in a cancer genomics context.

Conclusions:

  • Clearer understanding of hypothesis testing vs. binary classification aids data analysis.
  • The provided guidelines facilitate appropriate strategy selection for specific needs.
  • Informed strategy choice enhances the reliability of data-driven conclusions in research.