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Related Concept Videos

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...
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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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Decision Making: Traditional Method01:14

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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.
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Methods of Medium Optimization

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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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Decision Making: P-value Method01:09

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Evaluations of the Optimal Discovery Procedure for Multiple Testing.

Daniel B Rubin

    The International Journal of Biostatistics
    |May 27, 2016
    PubMed
    Summary

    The Optimal Discovery Procedure (ODP) offers simultaneous hypothesis testing but is less powerful than standard methods when controlling Type I error rates. Correlation negatively impacts ODP rankings compared to univariate p-values.

    Area of Science:

    • Statistics
    • Bioinformatics
    • Genomics

    Background:

    • Simultaneous hypothesis testing is crucial in multivariate analysis.
    • Standard methods like Bonferroni and Benjamini-Hochberg control specific error rates.
    • The Optimal Discovery Procedure (ODP) aims to improve power by leveraging multivariate data structure.

    Purpose of the Study:

    • To compare the statistical power of the ODP against Bonferroni and Benjamini-Hochberg methods.
    • To evaluate the ODP's effectiveness in testing hypotheses about Gaussian mean vectors.
    • To analyze the impact of correlation on ODP performance and rankings.

    Main Methods:

    • Theoretical analysis of Type I error control.
    • Numerical simulations comparing power across different methods.

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  • Application to two real-world microarray datasets.
  • Comparison of ODP test statistic rankings with standard p-value rankings.

    • Main Results:

    • When uniformly controlling Type I error rates, the ODP demonstrated lower power than competing methods.
    • The ODP's performance was negatively affected by correlation in the data.
    • Standard rankings based on univariate p-values generally outperformed ODP rankings.

    Conclusions:

    • The ODP may not offer a power advantage over standard methods when strict Type I error control is enforced.
    • Correlation is a significant factor that can diminish the utility of the ODP.
    • Further research may be needed to optimize ODP under various correlation structures.