DESCRIPTIVE STATISTICS

 DESCRIPTIVE STATISTICS

Descriptive statistics are informational and help to summarise and describe the actual characteristics of the data set. Descriptive statistics has three (3) basic categories.

1.     Measure of central tendency (mean, mode, median)

2.     Measure of variability i.e. spread of the data set (variance, standard deviation)

3.     Measures of frequency distribution, count of occurrence of each value (count)

To obtain descriptive statistics on your variables, the first step is to gather all relevant background information before conducting any statistical analyses. These descriptive statistics include the range, mean, standard deviation, skewness, and kurtosis. 

The second step is to check whether any assumptions of the individual tests are not violated. Checking assumptions is a crucial step in many statistical procedures to ensure that the data meets the requirements for valid application of the chosen statistical method.

  1. Normality Assumption:
    • Descriptive statistics, such as measures of central tendency (mean, median, mode) and measures of spread (standard deviation, range), help assess the normality assumption. For example, skewness and kurtosis values can provide insights into the shape of the distribution.
  2. Homogeneity of Variance:
    • In analyses such as Analysis of Variance (ANOVA) or t-tests, the assumption of homogeneity of variance is crucial. Descriptive statistics, including variance or standard deviation, are examined across different groups to check for homogeneity.
  3. Linearity:
    • For regression analyses, linearity is an important assumption. Descriptive statistics, scatterplots, and other graphical representations are used to assess the linear relationship between variables.
  4. Independence:
    • The assumption of independence is critical in various statistical tests. Descriptive statistics and study design are considered to ensure that observations are independent.
  5. Outlier Detection:
    • Descriptive statistics, such as the identification of extreme values (outliers), are important for assessing the impact of outliers on statistical analyses.
  6. Sample Size:
    • Descriptive statistics are often used to report the size of the sample, which can be important for determining whether the sample size is adequate for the statistical method being applied.





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