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Báo cáo y học: "Statistics review 1: Presenting and summarising data"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học General Psychiatry cung cấp cho các bạn kiến thức về ngành y đề tài: Statistics review 1: Presenting and summarising data. | Critical Care February 2002 Vol 6 No 1 Whitley and Ball Review Statistics review 1 Presenting and summarising data Elise Whitley and Jonathan Ball Lecturer in Medical Statistics University of Bristol Bristol UK Lecturer in Intensive Care Medicine St George s Hospital Medical School London UK Correspondence Editorial Office Critical Care editorial@ccforum.com Published online 29 November 2001 Critical Care 2002 6 66-71 2002 BioMed Central Ltd Print ISSN 1364-8535 Online ISSN 1466-609X Abstract The present review is the first in an ongoing guide to medical statistics using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data this is also an opportunity to look for unusually high or low values outliers to check the assumptions required for statistical tests and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data unordered and ordered and quantitative data discrete and continuous how these types of data can be represented figuratively the two important features of a quantitative dataset location and variability the measures of location mean median and mode the measures of variability range interquartile range standard deviation and variance common distributions of clinical data and simple transformations of positively skewed data. Keywords interquartile range mean median range standard deviation transformations unimodal distributions Data description is a vital part of any research project and should not be ignored in the rush to start testing hypotheses. There are many reasons for this important process such as gaining familiarity with the data looking for unusually high or low values outliers and checking the assumptions required for statistical .