Quinn Mcnemar
Quinn Mcnemar - Related Videos
01:23
McNemar's Test
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2025
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...
07:16
Isolation of Cancer Stem Cells From Human Prostate Cancer Samples
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Cited by 6 •
2014
The isolation of cancer stem cells (CSCs) directly from human tissues is requisite for their biological characterization. This manuscript describes a methodology for the isolation of prostate CSCs from human tissues, while also providing tips on troubleshooting difficult steps.
11:25
Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
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Cited by 29 •
2014
The reliability of results in metabolomics experiments depends on the effectiveness and reproducibility of the sample preparation. Described is a rigorous and in-depth method that enables extraction of metabolites from biological fluids with the option of subsequently analyzing up to thousands of compounds, or just the compound classes of interest.
07:52
Assessment of Murine Exercise Endurance Without the Use of a Shock Grid: An Alternative to Forced Exercise
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Cited by 37 •
2014
A method to assess exercise endurance in laboratory mice without the use of a shock grid is demonstrated. This method is a humane refinement that can decrease the confounding effects of stress on experimental parameters.
10:20
Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae
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Cited by 14 •
2015
Integration of microalgal cultivation with industrial flue gas will ultimately introduce heavy metals and other inorganic compounds into the growth media. This study presents a procedure used to determine the end fate and impact of heavy metals and inorganic contaminants on the growth of Nannochloropsis salina grown in photobioreactors.
FAQs
The test compares the two off-diagonal cells in the 2 × 2 table: units classified differently under the two conditions. Their imbalance indicates that changes favor one condition more than the other. Concordant pairs, which retain the same classification, add no directional evidence, so including them in the comparison would not address the paired difference directly.
Concordant outcomes are observations with the same category under both conditions, so they do not distinguish which condition produces more positive or negative classifications. They still belong in the study's paired dataset and table, but the inferential comparison is driven by the two types of change. This separation explains why the test can set aside their lack of change.
The exact binomial procedure is appropriate for small samples, where relying on a chi-square approximation may be less suitable. For larger paired datasets, the chi-square approximation provides the usual route described for the test. In either case, the calculation evaluates the same question: whether the two discordant counts support a difference in paired proportions.
First record each subject's or matched unit's classification under both conditions. Then cross-tabulate those paired results into a 2 × 2 contingency table, separating unchanged classifications from changes. The two off-diagonal counts become the critical inputs, while the diagonal counts document concordance. This organization preserves the pairing that the analysis requires.
Before-and-after studies, matched case-control designs, diagnostic test comparisons, and other repeated-measures research are appropriate settings. In each case, the outcome is recorded for the same subjects or matched units under two conditions, allowing changes within pairs to be examined. The method is especially relevant when the outcome is categorical and the research question concerns paired proportions.
A significant result indicates that the discordant classifications are sufficiently unbalanced to support a difference between the paired proportions. To determine the direction, inspect which off-diagonal cell is larger: that cell represents the more frequent type of change between conditions. Interpretation therefore combines the significance result with the observed pattern of changes in the 2 × 2 table.