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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

7.0K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
7.0K
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
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.4K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
4.4K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.0K
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

You might also read

Related Articles

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

Sort by
Same author

Maternal and congenital syphilis in Fiji 2019-2022: a secondary analysis of clinical trial data.

Epidemiology and infection·2026
Same author

Epidemiological, immunological and virulence characteristics of persistent Streptococcus pneumoniae vaccine serotypes following vaccine introduction.

Nature communications·2026
Same author

Fractional BNT162b2 boosters induce durable immune responses after non-mRNA priming in Mongolia: a randomised controlled trial.

Frontiers in immunology·2026
Same author

The Ideal Trial: Defining Causal Estimands that Balance Relevance and Feasibility in Target Trial Emulations and Actual Randomized Trials.

Epidemiology (Cambridge, Mass.)·2026
Same author

Evaluating the effectiveness of the 13-valent pneumococcal conjugate vaccine and clinical and demographic characteristics on pneumococcal carriage density in young children in Papua New Guinea, Lao PDR, and Mongolia.

BMC infectious diseases·2025
Same author

Pneumococcal carriage and disease in adults hospitalised with community-acquired pneumonia in Mongolia: prospective pneumonia surveillance program (2019-2022).

Pneumonia (Nathan Qld.)·2025
Same journal

Integrating health economics and implementation science: a call to action.

BMC medical research methodology·2026
Same journal

Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.

BMC medical research methodology·2026
Same journal

Sparse multi-way DMDC for longitudinal classification in high dimension low sample size data.

BMC medical research methodology·2026
Same journal

Tree-based exploratory identification of predictive biomarkers in non-randomized data.

BMC medical research methodology·2026
Same journal

Comparative evaluation of interrupted time series analytical methods for healthcare quality improvement research: a Monte Carlo simulation study.

BMC medical research methodology·2026
Same journal

Methodological advances in claims-based dementia algorithms: integrating medication and clinical data for medicare populations.

BMC medical research methodology·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K

Diagnosing problems with imputation models using the Kolmogorov-Smirnov test: a simulation study.

Cattram D Nguyen1, John B Carlin, Katherine J Lee

  • 1Clinical Epidemiology & Biostatistics Unit, Murdoch Childrens Research Institute, The Royal Children's Hospital, Flemington Road Parkville, Melbourne, Victoria 3052, Australia. cattram.nguyen@mcri.edu.au.

BMC Medical Research Methodology
|November 21, 2013
PubMed
Summary
This summary is machine-generated.

The Kolmogorov-Smirnov (KS) test can detect differences in imputed data but struggles to identify problematic multiple imputation (MI) models. Its sensitivity to sample size and missing data complicates its use as a reliable diagnostic tool.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.5K

Related Experiment Videos

Last Updated: May 5, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.5K

Area of Science:

  • Statistics
  • Data Science
  • Biostatistics

Background:

  • Multiple imputation (MI) is a common method for handling missing data.
  • Effective diagnostic tools for assessing MI model adequacy are scarce.
  • The Kolmogorov-Smirnov (KS) test is a potential method for evaluating imputed data distributions.

Purpose of the Study:

  • To evaluate the performance of the KS test as a diagnostic tool for multiple imputation.
  • To determine if the KS test can reliably detect assumption violations in imputation models.

Main Methods:

  • Simulations were used to assess the KS test's ability to identify imputation model departures.
  • The study examined KS test p-values with skewed and heavy-tailed data imputed via a normal model.
  • Variations included the amount of missing data, missing data models, and data skewness.

Main Results:

  • The KS test detected differences between observed and imputed values.
  • These differences did not consistently indicate issues with MI inference for regression parameters.
  • KS test p-values were highly sensitive to sample size and the proportion of missing data.
  • A strong missing at random dependency resulted in very small p-values, hindering discrimination of problematic imputations.

Conclusions:

  • Establishing clear guidelines for using the KS test as an MI diagnostic is challenging.
  • Further research into alternative imputation diagnostics and their software implementation is crucial.