Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range
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Abstract (arXiv)
In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al. (2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under more general settings where the first and third quartiles are also available for the trials. Through simulation studies, we demonstrate that the proposed methods greatly improve the existing methods and enrich the literature. We conclude our work with a summary table that serves as a comprehensive guidance for performing meta-analysis in different situations.
Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jul 2014.
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Citing papers per year: 2021: 1; 2022: 5; 2023: 3; 2024: 2; 2025: 2; 2026: 4.
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- 2026Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models9 Jun 2026
- 2026PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation27 Feb 2026
- 2025LeechHijack: Covert Computational Resource Exploitation in Intelligent Agent Systems2 Dec 2025
- 2025Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Software17 Oct 2025
- 2024Computer-mediated therapies for stroke rehabilitation: a systematic review and meta-Analysis30 Sep 2024
- 2024Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI24 Sep 2024
- 2023Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection20 Dec 2023
- 2023Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications21 Sep 2023
- 2023SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution6 Sep 2023
- 2022IAN: Iterated Adaptive Neighborhoods for manifold learning and dimensionality estimation19 Aug 2022
5 more
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- 2022Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation17 Jun 2022
- 2022Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement Learning13 May 2022
- 2022Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection28 Feb 2022
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- 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
- 2023Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detectioncited by 13
- 2023SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolutioncited by 12
- 2022IAN: Iterated Adaptive Neighborhoods for manifold learning and dimensionality estimationcited by 4
- 2024Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AIcited by 3
- 2022MACFE: A Meta-learning and Causality Based Feature Engineering Frameworkcited by 2
- 2022Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detectioncited by 1
- 2022Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentationcited by 1
- 2022Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement Learningcited by 0
- 2023Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classificationscited by 0
- 2024Computer-mediated therapies for stroke rehabilitation: a systematic review and meta-Analysiscited by 0
- 2025Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Softwarecited by 0
- 2025Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Software17 Oct 2025
- 2024Computer-mediated therapies for stroke rehabilitation: a systematic review and meta-Analysis30 Sep 2024
- 2024Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI24 Sep 2024
- 2023Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection20 Dec 2023
- 2023Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications21 Sep 2023
- 2023SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution6 Sep 2023
- 2022IAN: Iterated Adaptive Neighborhoods for manifold learning and dimensionality estimation19 Aug 2022
- 2022MACFE: A Meta-learning and Causality Based Feature Engineering Framework8 Jul 2022
- 2022Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation17 Jun 2022
- 2022Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement Learning13 May 2022
- 2022Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection28 Feb 2022
- 2021Generalized Out-of-Distribution Detection: A Survey21 Oct 2021