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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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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.
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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.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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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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Related Experiment Video

Updated: Jan 26, 2026

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory
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The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression.

Giovanna Cilluffo1, Gianluca Sottile2, Stefania La Grutta1

  • 1Institute of Biomedicine and Molecular Immunology, National Research Council, Palermo, Italy.

Statistical Methods in Medical Research
|April 18, 2019
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Summary

This study introduces a new lasso-type estimator for hypothesis testing with many covariates, enabling reliable p-values and statistical significance assessment in regression analysis.

Keywords:
Induced smoothingasthma researchlung functionsandwich formulasparse modelsvariable selection

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Area of Science:

  • Statistics
  • Machine Learning
  • Econometrics

Background:

  • Hypothesis testing for regression coefficients is crucial, especially with numerous covariates.
  • Existing methods in lasso regression may not provide reliable p-values for statistical inference.

Purpose of the Study:

  • To develop a novel lasso-type estimator for robust hypothesis testing.
  • To enable accurate statistical significance assessment for regression coefficients in high-dimensional settings.

Main Methods:

  • Proposing a new lasso-type estimator utilizing induced smoothing.
  • Developing methods for obtaining appropriate covariance matrices and Wald statistics.
  • Conducting simulation experiments to evaluate performance.

Main Results:

  • The proposed estimator yields reliable p-values for hypothesis testing.
  • The approach demonstrates good performance compared to existing inferential tools in lasso regression.
  • Real data analyses validate the practical applicability of the framework.

Conclusions:

  • The induced smoothing lasso-type estimator offers an effective solution for hypothesis testing in high-dimensional regression.
  • This method enhances the reliability of statistical inference in the presence of many covariates.