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Hypothesis testing, type I and type II errors
Open accessAbstract
Hypothesis testing is an important activity of empirical research and evidence-based medicine. A well worked up hypothesis is half the answer to the research question. For this, both knowledge of the subject derived from extensive review of the literature and working knowledge of basic statistical concepts are desirable. The present paper discusses the methods of working up a good hypothesis and statistical concepts of hypothesis testing.
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Citations
Cited by
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Davies, R. L., Prince, M. A., Bravo, A. J., Kelley, M. L., & Crain, T. L. (2019) · Journal of Traumatic Stress · doi:10.1002/jts.22403 · Cited by 26
Behavioral challenges to professional skepticism in auditors’ data analytics journey
Li, X. (2022) · Maandblad voor Accountancy en Bedrijfseconomie · doi:10.5117/mab.96.78525 · Cited by 11
Structure-Based Deep Learning Framework for Modeling Human–Gut Bacterial Protein Interactions
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Metadata
- Authors
- Banerjee, A., Chitnis, U., Jadhav, S., Bhawalkar, J., & Chaudhury, S.
- Venue
- Industrial Psychiatry Journal
- Published
- 2009
- Volume
- 18
- Issue
- 2
- Pages
- 127
- ISSN
- 0972-6748 (ISSN-L)
- OpenAlex
- W2000091716
- Cited by
- 435
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- gold
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How to cite
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- CoChat. (2026). Citation check: Hypothesis testing, type I and type II errors. https://app.cochat.ai/papers/10.4103/0972-6748.62274
- The paper
- Banerjee, A., Chitnis, U., Jadhav, S., Bhawalkar, J., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial Psychiatry Journal. https://doi.org/10.4103/0972-6748.62274
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
