Comparing statistical techniques

Depending on the nature of two sets of data n, s, sampling naturethe means of the sets can be compared for bias by several variants of the t-test. The following most common types will be discussed: Basically, for the t-tests Equation 6.

Comparing statistical techniques

Bose Road, Kolkata -India E-mail: This article has been cited by other articles in PMC. 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 authorsreviewers, editors and readers.

Comparing statistical techniques

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. 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 curvein 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.6 BASIC STATISTICAL TOOLS.

There are lies, damn lies, and statistics.. (Anon.) Introduction Definitions As stated previously, comparison of two methods using different levels of analyte gives more validation information about the methods than using only one level.

Generally, a statistical hypothesis test for comparing samples quantifies how likely it is to observe two data samples given the assumption that the samples have the same distribution.

Credibility: Evaluating What’s Been Learned, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Recommendations. Statistical technique for comparing means for multiple To compare the means in 2 groups, just use the methods we learned to conduct a hypothesis test for the equality of two population means (called a t-test) Partition the total variation in a response variable into.

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.

Methods of randomisation have been described in Chapter 3. The importance of randomisation is that we Imow in the long run treatment groups will be balanced in known and unknown prognostic factors.

The comparison of the blood pressure in farmers and printers given in Chapter 3 is an example of a case control study. Statistical Methods. This illustrates that statistical tests differ in strictness and that for proper interpretation of results in reports, the statistical techniques used, including the confidence limits or probability, should always be specified.

6 BASIC STATISTICAL TOOLS