Traditional statistics of Data (descriptive and inferential)

Traditional statistics  of Data 

Traditional statistics is made up of  descriptive and inferential statistics. Let’s explore the difference between descriptive statistics and inferential statistics.

Descriptive Statistics:

Descriptive statistics involves the summarization and presentation of data in a meaningful way. It provides insights into the characteristics of a dataset without making inferences beyond the data itself. Common measures used in descriptive statistics include:

Measures of central tendency: Mean, median, and mode, which represent the central or typical value of a dataset.

Measures of dispersion: Range, variance, and standard deviation, which quantify the spread or variability of data points.

Measures of shape: Skewness and kurtosis, which describe the symmetry and peakedness of a distribution, respectively.

Frequency distributions: Tables, histograms, and bar charts, which display the frequency or relative frequency of values within a dataset.

Graphs: Visual representations like histograms, boxplots, stem-and-leaf plots, and scatterplots help us understand data distribution.

Tables: Frequency tables, for instance, reveal how many data values fall within specific ranges.





Inferential Statistics:

Inferential statistics go beyond immediate data points. They use samples to make predictions or generalizations about larger populations.

These methods help us draw conclusions about a whole group based on a smaller subset of data.

For instance, if we want to estimate the average income of all working adults in a country, we might take a sample of individuals and use inferential statistics to make an

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