When you use the word “this is an estimate“, the first thing which comes in our mind that it is not accurate. YES, that is not actual data. There are two things.
First of all, we have to know what is accuracy and precision and what is the difference between. Let’s have a close look after that we will discuss on statistical estimation theory.
Accuracy means how much you close to your measurement which will come to the true value.
Precision means how much you close to a bunch of replicate measurements come to each other,
The inference we make about the population based on the information contained in the sample is called estimation. The population is categorized by numerical description measure objects to make an inference about one or more parameter with the help of simple statistics.
Point Estimation of a parameter:
The object of point estimation is to obtain a single number from the sample that is intended for estimating the unknown true value of a population parameter.
A point estimator is a sample statistic that is used to estimate the unknown true value of a population parameter.
An estimator is always a statistic with is both a function and a random variable with a probability distribution. An estimator is dominated by a capital letter (e.g T, U,…)
Percentage of something is the best example of point estimation.
a point estimate is a specific value of an estimator computed from the sample data after the sample has been observed. When a population and the estimator T is computed form the sample date, the numerical value obtained is an estimate of population parameter Q from the particular sample. An estimate is donated by a small letter (e.g t, u….)
Internal estimation is a percentage of constructing an interval from a random sample such that prior to sampling, it has a high specified probability of inducing the unknown true value of a population parameter.
The statical estimation is a procedure of making a judgement about the unknown true value of the population parameter by using the sample observation.