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

805
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,...
805
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.4K
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...
4.4K
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

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

6.2K
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...
6.2K
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.7K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.7K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.1K
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...
6.1K
Significance Testing: Overview01:04

Significance Testing: Overview

10.2K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
10.2K

You might also read

Related Articles

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

Sort by
Same author

School-Based Educational Interventions for Improving Knowledge of Back Health, Ergonomics and Postural Behaviour of School Children Aged 4-18 Years: A Best Evidence Systematic Review.

Campbell systematic reviews·2026
Same author

Test-Retest Reliability and Convergent Validity of Two Scoring Versions of the Spinal Appearance Questionnaire Against Radiographic Measurements and Established Quality of Life Questionnaires in Adolescents with Idiopathic Scoliosis.

Children (Basel, Switzerland)·2026
Same author

Postural Assessment: An Online Survey of Practicing Chiropractors in the UK.

Healthcare (Basel, Switzerland)·2025
Same author

Editorial: Insights in rehabilitation for musculoskeletal conditions 2023/2024.

Frontiers in rehabilitation sciences·2025
Same author

Do adolescents with different types and degrees of idiopathic scoliosis curves differ in postural control compared to their healthy peers? a cross-sectional study.

BMC musculoskeletal disorders·2024
Same author

Survey of Knowledge, Attitudes, and Levels of Confidence Regarding Age-Related Hyperkyphosis and Its Management among Thai Physiotherapists.

Healthcare (Basel, Switzerland)·2024

Related Experiment Video

Updated: May 7, 2026

Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization
08:13

Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization

Published on: May 18, 2020

8.0K

Selecting the most appropriate inferential statistical test for your quantitative research study.

Josette Bettany-Saltikov1, Victoria Jane Whittaker

  • 1Teesside University, Institute of Health and Social Care, Middlesbrough, UK.

Journal of Clinical Nursing
|October 10, 2013
PubMed
Summary

Selecting the correct statistical test is crucial for valid quantitative research. This paper guides researchers through the process, offering flowcharts to aid in choosing appropriate statistical methods for empirical studies.

Keywords:
inferentialquantitativeselectingstatistical test

More Related Videos

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

7.5K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.4K

Related Experiment Videos

Last Updated: May 7, 2026

Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization
08:13

Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization

Published on: May 18, 2020

8.0K
Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

7.5K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.4K

Area of Science:

  • Nursing Research
  • Biostatistics
  • Quantitative Methods

Background:

  • Quantitative nursing research relies on empirical data, requiring appropriate descriptive and inferential statistical tests.
  • Research designs and questions dictate the specific statistical tests needed for valid analysis.

Purpose of the Study:

  • To outline the critical issues and processes involved in selecting the most appropriate statistical test.
  • To provide guidance for nursing clinicians and researchers in choosing statistical methods for quantitative studies.

Main Methods:

  • Discursive paper format.
  • Review of basic research concepts.
  • Illustration with clinical scenarios.

Main Results:

  • Key considerations for selecting statistical tests in empirical quantitative studies are discussed.
  • Flowcharts are provided to aid in the statistical test selection process.

Conclusions:

  • Appropriate statistical test selection is essential for drawing valid conclusions in quantitative nursing research.
  • Careful consideration of research design and questions ensures the use of the most suitable statistical tests.