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Statistical tools cannot be neglected at all in answering research questions and testing hypotheses in quantitative research. Statistics are generally grouped into two - descriptive statistics and inferential statistics. While descriptive statistics describe, show, and summarize the basic features of a dataset about a sample, inferential statistics helps to describe and draw inferences about the population from which the sample is drawn.
There are four types of descriptive statistics, and there are measures of frequency, measures of central tendency, measures of variability or dispersion, and measures of position. Count, frequency, and percent are examples of measures of frequency; while mode, median, and mean are examples of measures of central tendency. Examples of measures of dispersion are range, variance, and standard deviation; while quartile, percentile, and z-score are examples of measures of position.
Inferential statistics has two types, which are parametric statistics and nonparametric statistics. While parametric statistics make some assumptions about the sample, nonparametric statistics do not make such assumptions. Examples of parametric statistics are t-tests, analysis of variance (ANOVA), analysis of covariance (ANCOVA), regressions, factor analysis, structural equation modeling, path analysis. Examples of nonparametric statistics are chi-square, McNema test, Wilcoxon, Kruska Wallis, Mann-Whitney U-test, runs test, sign tests.