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Related Concept Videos

Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
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Introduction to Test of Independence01:21

Introduction to Test of Independence

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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
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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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Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
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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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Related Experiment Video

Updated: Jun 24, 2025

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

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A Model-free Approach for Testing Association.

Saptarshi Chatterjee1, Shrabanti Chowdhury2, Sanjib Basu3

  • 1Eli Lilly and Company, Indianapolis, IN.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|June 12, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new maximal permutation test to identify serum biomarkers for non-small cell lung cancer (NSCLC) recurrence. The method is robust, model-free, and efficient for early-stage lung cancer patients.

Keywords:
Feature screeningLung CancerMaximal testPermutation testThresholding

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Area of Science:

  • Biostatistics
  • Oncology
  • Biomarker Discovery

Background:

  • Identifying biomarkers for non-small cell lung cancer (NSCLC) recurrence is crucial for early-stage patients.
  • Existing methods often rely on specific model assumptions, limiting their applicability.

Purpose of the Study:

  • To propose a general, assumption-free omnibus approach for testing outcome-feature associations.
  • To identify serum biomarkers predictive of recurrence in early-stage NSCLC patients.

Main Methods:

  • A maximal permutation test based on thresholding is proposed.
  • The method is computationally efficient and readily implementable.
  • Evaluated for detecting linear, nonlinear, and quantile-based associations under various distributions.

Main Results:

  • The proposed omnibus tests maintain significance levels and exhibit strong power.
  • Demonstrated effectiveness even with outlier-prone and heavy-tailed distributions.
  • Successfully applied to identify preoperative serum biomarkers for NSCLC recurrence.

Conclusions:

  • The maximal permutation test offers a robust and generalizable method for biomarker discovery.
  • This approach is valuable for identifying predictive biomarkers in early-stage NSCLC.
  • The method shows promise for model-free feature screening and binary outcomes.