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Related Concept Videos

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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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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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...
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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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Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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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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Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
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Multiple testing of hypotheses in comparing two groups.

L A Cupples, T Heeren, A Schatzkin

    Annals of Internal Medicine
    |January 1, 1984
    PubMed
    Summary
    This summary is machine-generated.

    When comparing two groups across multiple variables, avoid individual tests. Multivariate procedures like Hotelling

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    Area of Science:

    • Statistics
    • Biostatistics
    • Quantitative Research Methods

    Background:

    • Comparing two groups on numerous variables is common in research.
    • Individual hypothesis testing on each variable inflates Type I error rates due to ignoring variable correlations.

    Purpose of the Study:

    • To advocate for multivariate procedures over multiple univariate tests.
    • To introduce and compare Hotelling's T2, discriminant analysis, and logistic regression for group comparisons.
    • To discuss Bonferroni adjustment for preserving Type I error control.

    Main Methods:

    • Description of three multivariate procedures: Hotelling's T2, discriminant analysis, and logistic regression.
    • Discussion of assumptions, merits, and disadvantages of each method.
    • Explanation of Bonferroni adjustment for controlling Type I error.

    Main Results:

    • Multivariate methods provide a unified analysis, integrating all measures for a comprehensive comparison.
    • Each method has specific assumptions and applications, influencing their suitability.
    • Bonferroni adjustment can be applied post-multivariate analysis for individual variable error control.

    Conclusions:

    • Multivariate procedures are recommended for comparing two groups on multiple variables.
    • The choice of method depends on specific research circumstances and data characteristics.
    • Integrated analysis enhances statistical power and controls Type I error rates effectively.