Estimation is best defined as:
1. a process of inferring the values of unknown population parameters from those of known sample statistics
2. a process of inferring the values of unknown samples statistics from those of known population parameters
3. any procedure that views the parameter being estimated not as a constant, but, just like the estimator, as a random variable
4. a sampling procedure that matches each unit from population A with a “twin” from population B so that any sample observation about a unit in population A automatically yields an associated observation about a unit in population B
The best definition of estimation is option 1: a process of inferring the values of unknown population parameters from those of known sample statistics.
To understand how this definition applies, let's break it down:
Estimation involves inferring or making an educated guess about the values of unknown population parameters. In statistics, a population refers to a group of individuals, objects, or events being studied. A population parameter is a characteristic or measure of interest, such as the mean or proportion, that describes the entire population.
In order to estimate these unknown parameters, statisticians often use known sample statistics. A sample is a smaller subset of the population that is chosen to represent the larger population. Sample statistics are measures calculated from a sample, such as the sample mean or sample proportion.
The process of estimation involves using the available sample statistics to make an inference or best guess about the true values of the population parameters. This estimation is done through various statistical techniques and methods.
So, estimation is about using known sample statistics to make educated guesses about unknown population parameters. It is an important aspect of statistics and allows us to make inferences about a larger population based on a smaller sample.