File Name: which statistical test will be used and why .zip
Bose Road, Kolkata - , India E-mail: moc. Today statistics provides the basis for inference in most medical research. Yet, for want of exposure to statistical theory and practice, it continues to be regarded as the Achilles heel by all concerned in the loop of research and publication — the researchers authors , reviewers, editors and readers.
Published on January 28, by Rebecca Bevans. Revised on December 28, Statistical tests are used in hypothesis testing. They can be used to:.
Bose Road, Kolkata - , India E-mail: moc. Today statistics provides the basis for inference in most medical research. Yet, for want of exposure to statistical theory and practice, it continues to be regarded as the Achilles heel by all concerned in the loop of research and publication — the researchers authors , reviewers, editors and readers. Most of us are familiar to some degree with descriptive statistical measures such as those of central tendency and those of dispersion.
However, we falter at inferential statistics. This need not be the case, particularly with the widespread availability of powerful and at the same time user-friendly statistical software. As we have outlined below, a few fundamental considerations will lead one to select the appropriate statistical test for hypothesis testing.
However, it is important that the appropriate statistical analysis is decided before starting the study, at the stage of planning itself, and the sample size chosen is optimum.
These cannot be decided arbitrarily after the study is over and data have already been collected. The great majority of studies can be tackled through a basket of some 30 tests from over a that are in use. The test to be used depends upon the type of the research question being asked.
The other determining factors are the type of data being analyzed and the number of groups or data sets involved in the study. The following schemes, based on five generic research questions, should help.
Question 1: Is there a difference between groups that are unpaired? Groups or data sets are regarded as unpaired if there is no possibility of the values in one data set being related to or being influenced by the values in the other data sets. Different tests are required for quantitative or numerical data and qualitative or categorical data as shown in Fig.
For numerical data, it is important to decide if they follow the parameters of the normal distribution curve Gaussian curve , in which case parametric tests are applied. If distribution of the data is not normal or if one is not sure about the distribution, it is safer to use non-parametric tests.
When comparing more than two sets of numerical data, a multiple group comparison test such as one-way analysis of variance ANOVA or Kruskal-Wallis test should be used first. Repeatedly applying the t test or its non-parametric counterpart, the Mann-Whitney U test, to a multiple group situation increases the possibility of incorrectly rejecting the null hypothesis. Tests to address the question: Is there a difference between groups — unpaired parallel and independent groups situation?
Question 2: Is there a difference between groups which are paired? Pairing signifies that data sets are derived by repeated measurements e. Pairing will also occur if subject groups are different but values in one group are in some way linked or related to values in the other group e. A crossover study design also calls for the application of paired group tests for comparing the effects of different interventions on the same subjects.
Sometimes subjects are deliberately paired to match baseline characteristics such as age, sex, severity or duration of disease. A scheme similar to Fig. Once again, multiple data set comparison should be done through appropriate multiple group tests followed by post hoc tests. Tests to address the question: Is there a difference between groups — paired situation? Question 3: Is there any association between variables? The various tests applicable are outlined in Fig. It should be noted that the tests meant for numerical data are for testing the association between two variables.
These are correlation tests and they express the strength of the association as a correlation coefficient. An inverse correlation between two variables is depicted by a minus sign.
All correlation coefficients vary in magnitude from 0 no correlation at all to 1 perfect correlation. A perfect correlation may indicate but does not necessarily mean causality. When two numerical variables are linearly related to each other, a linear regression analysis can generate a mathematical equation, which can predict the dependent variable based on a given value of the independent variable. Logistic regression is actually a multivariate analysis method that expresses the strength of the association between a binary dependent variable and two or more independent variables as adjusted odds ratios.
Question 4: Is there agreement between data sets? This can be a comparison between a new screening technique against the standard test, new diagnostic test against the available gold standard or agreement between the ratings or scores given by different observers. As seen from Fig. It is inappropriate to infer agreement by showing that there is no statistically significant difference between means or by calculating a correlation coefficient.
Question 5: Is there a difference between time-to-event trends or survival plots? This question is specific to survival analysis[ 3 ] the endpoint for such analysis could be death or any event that can occur after a period of time which is characterized by censoring of data, meaning that a sizeable proportion of the original study subjects may not reach the endpoint in question by the time the study ends.
Data sets for survival trends are always considered to be non-parametric. It can be appreciated from the above outline that distinguishing between parametric and non-parametric data is important. Tests of normality e. Kolmogorov-Smirnov test or Shapiro-Wilk goodness of fit test may be applied rather than making assumptions.
Some of the other prerequisites of parametric tests are that samples have the same variance i. A one-tailed test calculates the possibility of deviation from the null hypothesis in a specific direction, whereas a two-tailed test calculates the possibility of deviation from the null hypothesis in either direction.
When Intervention A is compared with Intervention B in a clinical trail, the null hypothesis assumes there is no difference between the two interventions.
Deviation from this hypothesis can occur in favor of either intervention in a two-tailed test but in a one-tailed test it is presumed that only one intervention can show superiority over the other.
Although for a given data set, a one-tailed test will return a smaller p value than a two-tailed test, the latter is usually preferred unless there is a watertight case for one-tailed testing. It is obvious that we cannot refer to all statistical tests in one editorial. However, the schemes outlined will cover the hypothesis testing demands of the majority of observational as well as interventional studies. Finally one must remember that, there is no substitute to actually working hands-on with dummy or real data sets, and to seek the advice of a statistician, in order to learn the nuances of statistical hypothesis testing.
National Center for Biotechnology Information , U. Journal List Indian J Ophthalmol v. Indian J Ophthalmol. Barun K Nayak and Avijit Hazra 1. Barun K Nayak P. Author information Copyright and License information Disclaimer. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article has been cited by other articles in PMC. Open in a separate window. Figure 1. Figure 2. Figure 3. Tests to address the question: Is there an association between variables?
Figure 4. References 1. Research methodology simplified: Every clinician a researcher. New Delhi: Jaypee Brothers; Hypothesis testing and choice of statistical tests; pp. Petrie A, Sabin C, editors. Medical statistics at a glance. London: Blackwell Publishing; The theory of linear regression and performing a linear regression analysis; pp. Analysis of survival data. In: Wang D, Bakhai A, editors.
This page shows how to perform a number of statistical tests using SPSS. Each section gives a brief description of the aim of the statistical test, when it is used, an example showing the SPSS commands and SPSS often abbreviated output with a brief interpretation of the output. You can see the page Choosing the Correct Statistical Test for a table that shows an overview of when each test is appropriate to use. In deciding which test is appropriate to use, it is important to consider the type of variables that you have i. Most of the examples in this page will use a data file called hsb2, high school and beyond. This data file contains observations from a sample of high school students with demographic information about the students, such as their gender female , socio-economic status ses and ethnic background race. It also contains a number of scores on standardized tests, including tests of reading read , writing write , mathematics math and social studies socst.
Show more about author. DOI: Categories: Lessons in biostatistics. Journal Volume 20 February, Issue 1 Comparing groups for statistical differences: how to choose the right statistical test? Comparing groups for statistical differences: how to choose the right statistical test? Choosing the right statistical test may at times, be a very challenging task for a beginner in the field of biostatistics. This article will present a step by step guide about the test selection process used to compare two or more groups for statistical differences.
Our tutors are able to provide assistance with a wide array of topics, ranging from the fundamentals to more advanced statistical concepts. Below is a non-exhaustive list of topics that we are able to help with. Understanding the fundamentals of statistics is imperative if you intend to go on, and draw meaningful conclusions from the analysis of data. These relatively basic initial steps should not be underestimated, as any confusion at this level will ultimately lead on to incorrect or meaningless inference. This section of the website contains resources which focus mainly on the fundamentals i. Summarizing an important feature of a set of data using just one number i. We may be interested in the probability of a bus turning up in the next ten minutes, or the chance of drawing a full house in poker.
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Test if this correlation is different from r12, if r23 is specified, but r13 is not, then r34 becomes r