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Related Concept Videos

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Statistical learning and selective inference.

Jonathan Taylor1, Robert J Tibshirani2

  • 1Department of Statistics, Stanford University, Stanford, CA 94305;

Proceedings of the National Academy of Sciences of the United States of America
|June 24, 2015
PubMed
Summary
This summary is machine-generated.

Selective inference addresses challenges in assessing data associations after data mining. New methods ensure rigorous statistical significance testing for findings from large datasets and complex models.

Keywords:
P valuesinferencelasso

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Area of Science:

  • Statistics
  • Data Science
  • Machine Learning

Background:

  • Data mining and statistical learning methods allow for sophisticated analysis of large datasets.
  • Identifying potential associations in data is a crucial first step in many scientific investigations.

Purpose of the Study:

  • To introduce and explain the problem of selective inference.
  • To present recent developments in selective inference methodologies.
  • To demonstrate the application of these methods in common statistical learning techniques.

Main Methods:

  • The study discusses the theoretical underpinnings of selective inference.
  • Illustrative examples are provided using forward stepwise regression.
  • Applications are shown for the lasso and principal components analysis.

Main Results:

  • Selective inference provides a framework for valid statistical assessment after data exploration.
  • Higher statistical bars are necessary to declare significance when data has been pre-selected.
  • New developments offer practical solutions for complex modeling scenarios.

Conclusions:

  • Proper assessment of statistical significance is critical, especially after data-driven discovery.
  • Selective inference methods are essential for reliable conclusions in the era of big data.
  • The presented techniques enhance the trustworthiness of findings from statistical learning.