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Related Concept Videos

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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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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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.
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The sign test is a nonparametric method used to evaluate hypotheses about the median of a single sample or to compare the medians of two related samples. The sign test is particularly useful when dealing with nominal data, which includes distinct categories without an inherent order, such as names, labels, and preferences. Nominal data restricts statistical analysis to evaluating population proportions rather than mean or median values that require continuous data.
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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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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.
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Related Experiment Video

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Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
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Global tests for novelty.

Ilmari Ahonen1,2, Denis Larocque3, Jaakko Nevalainen1,4

  • 11 Department of Mathematics and Statistics, University of Turku, Finland.

Statistical Methods in Medical Research
|July 9, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces novel hypothesis tests for global novelty detection, identifying exceptional patterns in new data. These methods are validated for detecting various novelty types in real-world applications.

Keywords:
Novelty detectionhigh-content screeninghypothesis testnonparametric statisticspermutation test

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Area of Science:

  • Statistics
  • Machine Learning
  • Bioinformatics

Background:

  • Outlier detection identifies unusual observations, while novelty detection finds exceptional new data points compared to training data.
  • Often, the presence of novelty itself is more critical than pinpointing individual novel instances, such as in screening new cancer treatments.

Purpose of the Study:

  • To develop and validate hypothesis tests for global level novelty detection.
  • To introduce innovative methods applicable under general assumptions, advancing current literature.

Main Methods:

  • Development of novel test statistics operating on local neighborhoods.
  • Utilizing the permutation principle to derive the null distribution of the test statistics.
  • Assessing method validity and performance through simulations and real-world data analysis.

Main Results:

  • The proposed tests are shown to be valid for detecting novelty.
  • The methods successfully identify different types of novelty, including location and scale alternatives.
  • Performance evaluation confirms the efficacy of the developed novelty detection techniques.

Conclusions:

  • The presented hypothesis tests offer a robust approach to global novelty detection.
  • These methods provide a valuable tool for applications where identifying the existence of new patterns is crucial.
  • The innovative framework broadens the scope of novelty detection methodologies.