Basic Statistics
Testing differences in proportions
This paper is the sixth in a series of statistics articles recently published by Australian Critical Care. In this paper we explore the most commonly used statistical tests to compare groups of data at the nominal level of measurement. The chosen statistical tests are the chi-square test, chi-square test for goodness of fit, chi-square test for independence, Fisher's exact test, McNemar's test and the use of confidence intervals for proportions. Examples of how to use and interpret the tests are provided.Testing differences between two samples of continuous data
In this article the circumstances and techniques used to test a hypothesis by comparing information from two random samples to identify possible or existing difference amongst the target population are presented. This is based on continuous data collected from two samples after which a comparison can be made that may then be generalised to the source population. The terminology of the method is briefly explained including basic concepts such as survey errors and probabilistic chance. Hypothesis test methods are described for two types of samples: independent and dependent, and examples of some of the most commonly used test in health research are given for parametric and non-parametric source distributions.Statistical and clinical significance, and how to use confidence intervals to help interpret both
Statistical significance is a statement about the likelihood of findings being due to chance. Classical significance testing, with its reliance on p values, can only provide a dichotomous result – statistically significant, or not. Limiting interpretation of research results to p values means that researchers may either overestimate or underestimate the meaning of their results. Very often the aim of clinical research is to trial an intervention with the intention that results based on a sample will generalise to the wider population.Understanding descriptive statistics
There is an increasing expectation that critical care nurses use clinical research when making decisions about patient care. This article is the second in a series which addresses statistics for clinical nursing practice. In this article we provide an introduction to the use of descriptive statistics. Concepts such as levels of measurement, measures of central tendency and dispersion are described and their use in clinical practice is illustrated.Statistics for clinical nursing practice: An introduction
Difficulty in understanding statistics is one of the most frequently reported barriers to nurses applying research results in their practice. Yet the amount of nursing research published each year continues to grow, as does the expectation that nurses will undertake practice based on this evidence. Critical care nurses do not need to be statisticians, but they do need to develop a working knowledge of statistics so they can be informed consumers of research and so practice can evolve and improve.
