Large Sample Methods :
Large sample methods are statistical techniques used to analyze data sets with a large number of observations. These methods are useful for making inferences about a population based on the characteristics of a sample. There are two main types of large sample methods: hypothesis testing and estimation.
Hypothesis testing is a statistical procedure that involves testing a specific claim or assumption about a population. This method is used to determine whether a statistical relationship exists between two variables in a sample. For example, a researcher might use hypothesis testing to determine whether there is a significant difference in the average income of men and women in a particular population. In this case, the null hypothesis would be that there is no difference in average income between men and women, and the alternative hypothesis would be that there is a difference. The researcher would then use statistical tests to determine the likelihood of the null hypothesis being true.
Estimation is a statistical procedure that involves using sample data to make inferences about the characteristics of a population. This method is used to determine the value of a population parameter, such as the mean or median. For example, a researcher might use estimation to determine the average height of adults in a particular population. In this case, the researcher would collect a sample of adults and measure their heights. The researcher would then use statistical techniques to calculate the average height of the sample, and use this value to estimate the average height of the population.
Large sample methods are useful for making inferences about a population because they allow researchers to draw conclusions based on a large amount of data. This allows researchers to make more accurate and precise inferences about the population, which can be particularly useful when working with large and complex data sets. Additionally, large sample methods can help researchers to identify and control for potential sources of bias and error in their analyses, which can help to improve the validity and reliability of their findings.