Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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, comparing...

Statistical Hypothesis Testing

Statistical Hypothesis Testing

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...

Statistical Significance

Statistical Significance

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...

Choosing Between z and t Distribution

Choosing Between z and t Distribution

The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Understanding Economic Decision-Making in Digital Therapeutics Development: Qualitative Approach.

Journal of medical Internet research·2025

Same author

A group concept mapping study of stakeholder perspectives on digital therapeutics economic value drivers.

NPJ digital medicine·2025

Same author

Economic Evaluation Associated With Clinical-Grade Mobile App-Based Digital Therapeutic Interventions: Systematic Review.

Journal of medical Internet research·2023

Same author

Costing bias in economic evaluations.

Journal of medical economics·2015

Same author

Needed: system dynamics for the drug discovery process.

Drug discovery today·2008

Same author

Glycosylation of HIV-1 gp120 V3 loop: towards the rational design of a synthetic carbohydrate vaccine.

Current medicinal chemistry·2008

Search research articles

Related Experiment Video

Updated: Jul 4, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Bayesian versus Frequentist statistical modeling: a debate for hit selection from HTS campaigns.

L Martin Cloutier¹, Suzanne Sirois

¹Department of Management and Technology, Room R-3570, School of Management, University of Quebec at Montreal, 315 Ste. Catherine East, Montreal, QC H2X 3X2, Canada.

Drug Discovery Today

|June 14, 2008

Summary

The Bayesian-Frequentist debate influences hit prioritization in high-throughput screening (HTS) for drug discovery. Statistical analyses in early drug development impact later stages, with both approaches aiming for similar outcomes despite increasing data complexity.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jul 4, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Pharmacology and Drug Discovery
Statistical Methodology in Research

Background:

The Bayesian-Frequentist debate represents fundamental differences in statistical inference and research philosophy.
Early-stage decisions in drug discovery, including hit selection from high-throughput screening (HTS), significantly affect later development success.
The increasing volume and complexity of data from HTS campaigns necessitate advanced statistical analysis for effective hit prioritization.

Purpose of the Study:

To review recent statistical analysis advancements for hit selection in drug discovery.
To explore the role and potential impact of the Bayesian-Frequentist debate on prioritizing hits from HTS campaigns.
To assess how statistical approaches align with the growing information content in HTS data over time.

Main Methods:

Literature review of current statistical analyses employed in drug discovery hit selection.
Analysis of the influence of early-stage decision-making on overall HTS performance.
Examination of the convergence and divergence of Bayesian and Frequentist approaches in the context of HTS data.

Main Results:

Recent statistical methods are being applied to improve hit selection in drug discovery.
Decisions made early in the drug discovery pipeline have a substantial effect on subsequent HTS performance.
While Bayesian and Frequentist statistical approaches strive for consensus, their ability to provide identical answers may be limited as HTS data value increases.

Conclusions:

The Bayesian-Frequentist debate is increasingly relevant to hit prioritization in HTS.
Effective statistical analysis is crucial for optimizing drug discovery pipelines by informing early-stage decisions.
Further research is needed to fully understand the implications of these statistical debates for successful drug development.