Parametric statistics is a branch of statistics that deals with data that follows a specific distribution, such as the normal distribution. It involves making assumptions about the underlying population and using mathematical models to analyze the data.
The basic concepts of parametric statistics include measures of central tendency, measures of variability, hypothesis testing, and confidence intervals. These concepts are used to summarize and analyze data in a meaningful way.
Parametric statistics allows for more accurate and precise analysis of data, as it takes into account the underlying distribution of the data. It also allows for a wider range of statistical tests to be used and can provide more powerful results.
One of the main challenges of using parametric statistics is the assumption of a specific distribution for the data. If the data does not follow this distribution, the results may not be reliable. Additionally, parametric statistics may not be suitable for small sample sizes or non-normal data.
Parametric statistics is commonly used in fields such as psychology, biology, and economics to analyze data and test hypotheses. It is also used in quality control and manufacturing to monitor and improve processes.