ArticleIndian journal of thoracic and cardiovascular surgery2025
Inferential statistics for cardiothoracic surgeons: Part 3 - drawing valid conclusions from clinical data.
Article in Indian journal of thoracic and cardiovascular surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Immersive Tai Chi for Home-Based Exercise in Older Adults: Usability and Feasibility Study.JMIR serious games · 2026Article
- Genotoxic exposures to volatile organic compounds in golden retrievers with and without multicentric lymphoma.Frontiers in veterinary science · 2026Article
- Regression analysis for cardiothoracic surgeons: part 5-predicting risks and outcomes in surgery.Indian journal of thoracic and cardiovascular surgery · 2025Article
- Correlation and causation for cardiothoracic surgeons: part 4-distinguishing relationships in data.Indian journal of thoracic and cardiovascular surgery · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferential statistics enable researchers to make predictions about a population based on sample data. This involves hypothesis testing where the null hypothesis assumes no effect, and the alternative hypothesis suggests a significant effect. Testing requires assumptions like normality and independence to be validated using tests like Shapiro-Wilk or Levene's for normality and variance. Significant findings are determined by p-values, with values under 0.05 typically indicating non-random effects. Choosing between parametric and non-parametric tests depends on data normality and variance homogeneity. Tools such as t-tests, analysis of variance (ANOVA), and their non-parametric counterparts like Mann-Whitney or Kruskal-Wallis are used based on these criteria, ensuring appropriate conclusions about clinical effects and interventions.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.