 Difference between Descriptive and Inferential Statistics - Do my Statistics Homework - Stats Answers Solver

# Difference between Descriptive and Inferential Statistics

## Descriptive statistics

As the name suggests, descriptive statistics involves description of data using typical values. It is often divided into three; Measures of frequency, measures of central tendency and measures of dispersion. Measures of frequency are quantities that describe the data as a percentage of the total count. For instance, it is through the measures of central tendency that we are able to know that in a given data there were 60% males and 40% female respondents.

Measures of central tendencies are the representative quantities of a data. These measure include the mean, mode and the median.

Measures of dispersion on the other hand tell us how a given data is dispersed. The measures of dispersion are the standard deviation and the range. Lastly we have the measures of position. These category involves the percentile ranks and quartile ranks.

## Inferential Statistics

Inferential statistics is the most critical branch of Statistics that mainly uses sample data drawn from a given population. This sample data is used to describe and make inferences about the population. The step-by-step process of inferential statistics is

• Identification of the research question- The research question simply refers to the main reason as to why a given study is being undertaken.
• Formulation of the hypothesis to address the research question – This is where the researcher comes up with the null and alternative hypothesis. The null hypothesis is usually the opposite of the alternative hypothesis. The alternative hypothesis is usually what the researcher wants to prove or achieve through the study. For further online help search check our homepage.
• Identification of the proper statistical tests to test the proposed hypotheses – In statistics there are two types of statistical tests: parametric and non-parametric tests. The only difference between parametric and non-parametric tests is that parametric test makes assumptions about the sample population while non-parametric test makes no such assumptions. When the assumptions are correct, parametric methods often produce accurate and distinctive estimates as compared to non-parametric methods. This is simply referred to as the statistical power. Some of the assumptions commonly made by the parametric test include: linearity of the variables, normality (assumes that the variables approximately follow a normal distribution) and homogeneity of variances. Examples of parametric test include such as regression test, Anova test and correlation tests amongst others. Examples of non-parametric tests include Kolmogorov Smirnov test, Friedman’s Anova test, Chi square test of independence and Mann-Whitney test amongst others.
• Performing the statistical tests
• Reporting the results-Results are often presented in neatly well-labelled tables.
• Interpreting the data analysis results- It is always important that the results presented to be interpreted well to avoid misleading any reader. The interpretation should be explicit leaving no stone unturned.
• Drawing a conclusion from the data analysis about the population from which the sample was taken

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