Basic Statistical Tools in Research and Data Analysis

Abstract

Statistical methods involved in carrying out the study consists of planning, designing, data
collection, analyze the data, drawing suitable interpretation and reporting the findings. For
each research work, statistical methods are used for the analysis and interpretation of the
data. In the selection of a proper statistical method for data analysis, the researcher has a
concrete idea about the assumption and conditions of the statistical methods. The results and
inferences are precise only if proper statistical tests are used. This article will try to update
the reader with the basic research tools that are utilized while conducting various studies.
The two most well-known statistical methods use in data analysis are descriptive statistics
and inferential statistics. Descriptive statistics summarize and organize the characteristics
of a data set using various measures such as mean, median, variance, etc. while inferential
statistics help to come to conclusions from sample data using statistical tests such as t-test, F
-test, etc. The selection of a proper statistical method depends on the objective of the research
study, type of data, type of distribution and nature of the observations. Methods that use
distributional assumptions are called parametric methods because we estimate the parameters
of the distribution assumed for the data. The parametric statistical methods are used to
compare the means while methods that do not require us to make distributional assumptions
about the data, such as the rank methods, are called non-parametric. Nonparametric methods
are used to compare other than means like proportions, medians, etc. This article covers a
brief outline of parametric and nonparametric methods, the assumption in these methods
and the selection of appropriate statistical tests for analysis and interpretation of the survey
data.