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SPECIAL ARTICLE
Year : 2020  |  Volume : 3  |  Issue : 2  |  Page : 76-84

Study design, errors and sample size calculation in medical research


1 Department of Anaesthesiology, Medical College, Kolkata, West Bengal, India
2 Department of Anaesthesiology, ESI PGIMSR, Bengaluru, Karnataka, India
3 Department of Anaesthesia, Dharwad Institute of Mental Health and Neurosciences, Dharwad, Karnataka, India
4 Department of Onco-Anaesthesia and Palliative Medicine, Dr BRAIRCH, AIIMS, New Delhi, India
5 Department of Anaesthesia, Vijayanagara Institute of Medical Sciences, Bellary, Karnataka, India

Correspondence Address:
Dr. Umesh Goneppanavar
Dharwad Institute of Mental Health and Neurosciences, Dharwad, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ARWY.ARWY_29_20

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The choice of an appropriate study design is one of the crucial steps in the research process after framing a research question. A single research question may fit into different study designs. Each design has its own merits and drawbacks; diligence in implementing the methodology and data collection reflects good study design. Sample size justification and power analysis are foundations of a study design. They should ideally be settled when framing a research question and creating the study design. An adequate sample size minimises random error or chance occurrence. 'A just large enough' sample supports the researcher to estimate expected cost, time and feasibility. The sample 'size' is a tug-of-war between reality and scientific effectiveness and is highly influenced by study designs. Null hypothesis (H0) is the assumption that there is no difference in the treatment groups, whereas an assumption that there is a difference is called alternate hypothesis (Ha). Type I error (α) finds difference in the absence of one (false-positive conclusion), whereas Type II error (β) indicates probability of false-negative results. If the calculated P value is smaller than α, the researcher rejects the null hypothesis (H0) and welcomes the alternative hypothesis (Ha). There are several validated software available for sample size calculation. Sample size tends to be smaller for means than percentages. As the sample size increases, the P value tends to become small. Finally, a statistically significant result might not always be clinically relevant.


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