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Related Concept Videos

P-value01:10

P-value

7.1K
P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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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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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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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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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
479
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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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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p-Value simulation for affected sib pair multiple testing.

Xiaoling Wu1, Daniel Q Naiman

  • 1Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, MD 21218, USA.

Human Heredity
|July 15, 2005
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for calculating critical values in genetic linkage analysis, improving accuracy for small sample sizes and numerous markers. The method avoids Gaussian approximations, offering more reliable p-values in complex scenarios.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Standard methods for affected sib pair multiple testing rely on Gaussian approximations.
  • These approximations perform well for large sample sizes but may be inaccurate for small samples or many markers.

Purpose of the Study:

  • To develop a novel algorithm for calculating multiple testing p-values in genetic linkage analysis.
  • To overcome limitations of Gaussian approximations in scenarios with small sample sizes, numerous markers, and small p-values.

Main Methods:

  • Developed a Markov chain model for inheritance vectors, avoiding Gaussian approximations.
  • Implemented an algorithm for precise p-value calculation in affected sib pair studies.

Main Results:

  • The new algorithm provides more accurate critical values compared to Gaussian approximations, especially for small sample sizes.
  • Demonstrated inadequacies of the Gaussian approximation in specific genetic analysis scenarios.

Conclusions:

  • The developed algorithm offers a more robust approach to multiple testing p-value calculation in genetic studies.
  • This method enhances the reliability of findings in complex genetic analyses with limited data or high marker density.