Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
McNemar's Test01:23

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
Statistical Hypothesis Testing01:16

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...

You might also read

Related Articles

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

Sort by
Same author

Genetic findings in people with schwannomas who do not meet clinical diagnostic criteria for <i>NF2</i>-related schwannomatosis.

Journal of medical genetics·2024
Same author

Screening of potential novel candidate genes in schwannomatosis patients.

Human mutation·2022
Same author

Re-evaluation of missense variant classifications in NF2.

Human mutation·2022
Same author

Sporadic vestibular schwannoma: a molecular testing summary.

Journal of medical genetics·2020
Same author

Incidence of mosaicism in 1055 de novo NF2 cases: much higher than previous estimates with high utility of next-generation sequencing.

Genetics in medicine : official journal of the American College of Medical Genetics·2019
Same author

The relative success of recognition-based inference in multichoice decisions.

Cognitive science·2011
Same journal

Proficiency order invariance of MLE, MAP, EAP, and WLE in item response theory.

The British journal of mathematical and statistical psychology·2026
Same journal

Bias and precision in true-score estimation.

The British journal of mathematical and statistical psychology·2026
Same journal

Polychoric correlations under the assumption of elliptical latent traits.

The British journal of mathematical and statistical psychology·2026
Same journal

Regularized reduced rank regression for mixed predictor and response variables.

The British journal of mathematical and statistical psychology·2026
Same journal

A multiple-choice SDT model for cognitive diagnosis models.

The British journal of mathematical and statistical psychology·2026
Same journal

Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling.

The British journal of mathematical and statistical psychology·2026
See all related articles

Related Experiment Videos

Ranald Macdonald and statistical inference.

Philip T Smith1

  • 1School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK. p.t.smith@reading.ac.uk

The British Journal of Mathematical and Statistical Psychology
|April 9, 2009
PubMed
Summary
This summary is machine-generated.

Ranald Roderick Macdonald advanced mathematical psychology by focusing on significance testing. His work advocated for the

Related Experiment Videos

Area of Science:

  • Mathematical psychology
  • Statistical inference
  • Foundations of statistics

Background:

  • Ranald Roderick Macdonald's significant contributions to mathematical psychology in the UK.
  • His active roles in the British Journal of Mathematical and Statistical Psychology and the British Psychological Society.
  • Focus on the interpretation of significance testing results.

Discussion:

  • Critique of traditional significance testing methods.
  • Advocacy for the 'Weak Fisherian' approach to hypothesis testing.
  • Emphasis on relevant information for interpreting statistical results.

Key Insights:

  • Macdonald's work highlighted the importance of relevant information in significance testing.
  • He championed a nuanced approach to hypothesis testing, moving beyond simple null hypothesis significance testing.
  • His contributions influenced the understanding and application of statistical methods in psychology.

Outlook:

  • Continued relevance of Macdonald's foundational work in statistical inference.
  • Potential for further research into the 'Weak Fisherian' hypothesis testing framework.
  • The enduring impact of his advocacy on statistical practices in psychological research.