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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

4.7K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.7K
Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.2K
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%...
710
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

3.3K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
3.3K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

665
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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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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False Discovery Control in Large-Scale Spatial Multiple Testing.

Wenguang Sun1, Brian J Reich2, T Tony Cai3

  • 1University of Southern California, Los Angeles, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|February 3, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for controlling false discoveries in spatial data analysis. It offers improved accuracy and power for identifying significant spatial signals, like ozone trends.

Keywords:
Compound decision theoryfalse cluster ratefalse discovery exceedancefalse discovery ratelarge-scale multiple testingspatial dependency

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Area of Science:

  • Spatial statistics
  • Statistical inference
  • Environmental science

Background:

  • Multiple testing is crucial for analyzing spatial data.
  • Existing methods struggle with accurate false discovery control in continuous spatial domains.
  • Identifying true spatial signals requires robust statistical frameworks.

Purpose of the Study:

  • To develop a unified theoretical and computational framework for false discovery control in spatial signal analysis.
  • To introduce optimal procedures for point-wise and cluster-wise spatial analyses.
  • To enable accurate analysis of large spatial datasets, such as environmental trends.

Main Methods:

  • Derivation of oracle procedures for controlling false discovery rate, false discovery exceedance, and false cluster rate.
  • Development of a data-driven finite approximation strategy for continuous spatial domains.
  • Implementation using Bayesian computational algorithms for asymptotic validity.

Main Results:

  • Proposed procedures offer optimal control over various false discovery metrics.
  • Data-driven approximation effectively mimics oracle procedures in continuous space.
  • Bayesian algorithms facilitate analysis of large spatial datasets.

Conclusions:

  • The new framework provides superior error control and power compared to conventional methods.
  • The methods are effective for analyzing spatio-temporal trends, demonstrated with tropospheric ozone data.
  • This approach enhances the reliability of spatial signal detection in complex datasets.