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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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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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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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Multiple Comparison Tests01:13

Multiple Comparison Tests

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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.
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
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

13.8K
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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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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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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A region-based multiple testing method for hypotheses ordered in space or time.

Rosa J Meijer, Thijmen J P Krebs, Jelle J Goeman

    Statistical Applications in Genetics and Molecular Biology
    |December 20, 2014
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    Summary
    This summary is machine-generated.

    This study introduces a novel multiple testing method for ordered hypotheses, efficiently testing all individual and region hypotheses simultaneously to control error rates. The R package cherry implements this approach for analyzing spatial or temporal data, such as DNA copy number variations.

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    Area of Science:

    • Statistical genetics
    • Bioinformatics
    • Genomic data analysis

    Background:

    • Multiple testing is crucial for analyzing large-scale datasets, especially those with spatial or temporal ordering.
    • Identifying localized effects within ordered data requires specialized statistical approaches beyond simple individual hypothesis tests.

    Purpose of the Study:

    • To develop a unified multiple testing procedure for hypotheses ordered in space or time.
    • To enable simultaneous testing of individual and contiguous region hypotheses, controlling the familywise error rate.
    • To provide a flexible method when the number and extent of potentially interesting regions are unknown.

    Main Methods:

    • A novel multiple testing procedure is proposed that integrates testing of all elementary and region hypotheses.
    • The method initiates with a global null hypothesis test, proceeding to localized tests upon rejection.
    • Implementation is provided via the R package 'cherry', facilitating practical application.

    Main Results:

    • The method effectively controls the familywise error rate across all tested hypotheses (individual and regional).
    • Demonstrated utility on a DNA copy number dataset, highlighting its applicability in real-world genomic studies.
    • The approach allows for the detection of signals within specific regions, even if individual markers are not significant.

    Conclusions:

    • The proposed method offers a powerful and comprehensive framework for multiple testing in ordered data.
    • It enhances the ability to detect and localize signals in spatial or temporal datasets.
    • The 'cherry' R package provides accessible implementation for researchers in genomics and related fields.