Descriptive statistics is a data analysis technique used in quantitative research to summarize and describe the main features of a dataset. It involves organizing, summarizing, and presenting data in a meaningful way to provide insights and understand patterns or trends.
Some common descriptive statistics techniques include:

Measures of central tendency: These statistics provide information about the center or average of a dataset. The most commonly used measures of central tendency are the mean (average), median (middle value), and mode (most frequently occurring value).

Measures of dispersion: These statistics provide information about the spread or variability of a dataset. Common measures of dispersion include the range (difference between the maximum and minimum values), variance (average of squared differences from the mean), and standard deviation (square root of the variance).

Frequency distribution: This technique involves organizing data into groups or categories and counting the number of observations in each group. It helps in understanding the distribution of data and identifying any patterns or outliers.

Percentiles: Percentiles divide a dataset into equal parts, indicating the percentage of values that fall below a certain value. For example, the 75th percentile represents the value below which 75% of the data falls.

Correlation analysis: This technique measures the strength and direction of the relationship between two variables. It helps in understanding the degree to which changes in one variable are associated with changes in another variable.
Descriptive statistics provide a summary of the data and help researchers to better understand and interpret their findings. They are often used as a preliminary analysis before conducting more advanced statistical techniques.