7.1:
What are Estimates?
Recall that descriptive statistics uses a basic summary statistic, such as measures of central tendency and standard deviation, to describe what given data show.
Inferential statistics or inductive statistics, on the other hand, helps to draw conclusions based on such measures of the given data.
It is, however, often impossible to collect data on a parameter of the entire population.
Instead, we draw samples that would accurately represent the population and collect data on the desired parameter using these samples.
The parameter value obtained from this collected data from the samples is the estimate of that population parameter.
Studying the estimates—such as proportion, mean, or variance—uses the standard scores, which are more commonly known as the z scores.
Estimates are quite crucial in hypothesis-testing and all the associated statistical operations.
Methods of estimation also provide a scientific framework to design experiments more precisely and conduct meta-analyses.
7.1:
What are Estimates?
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates.
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean, proportion, and variance of samples are measured using standard scores, commonly called z scores. Estimates are essential for hypothesis testing, and methods of estimation are used when designing experiments and conducting meta-analyses.