When we think of the term "population," we usually think of people in our town, region, state or country and their respective characteristics such as gender, age, marital status, ethnic membership, religion and so forth. In statistics the term "population" takes on a slightly different meaning. The "population" in statistics includes all members of a defined group that we are studying or collecting information on for data driven decisions.
A part of the population is called a sample. It is a proportion of the population, a slice of it, a part of it and all its characteristics. A sample is a scientifically drawn group that actually possesses the same characteristics as the population - if it is drawn randomly.(This may be hard for you to believe, but it is true!)
You are studying the sample because you want to find out information about the whole population. If the sample you have drawn from the population does not represent the population, you will find out about the sample but will not find out about the population.
Data is neither sample nor population. Data are collected for attributes. These can be for a sample or a population.
That the key characteristics of the population are reflected in the sample.
From a sample of a population, the properties of the population can be inferred.
A sample of a population is a subset of the population. The average of the population is a statistical measure for some variable of the population.
A Sample
A sample is a subset of the population.
You are studying the sample because you want to find out information about the whole population. If the sample you have drawn from the population does not represent the population, you will find out about the sample but will not find out about the population.
A sample is a subset of the population.
The sample is a subset of the population.
Data is neither sample nor population. Data are collected for attributes. These can be for a sample or a population.
It is not a sample. A sample must be a proper subset of the whole population.
It can get a bit confusing! The estimate is the value obtained from a sample. The estimator, as used in statistics, is the method used. There's one more, the estimand, which is the population parameter. If we have an unbiased estimator, then after sampling many times, or with a large sample, we should have an estimate which is close to the estimand. I will give you an example. I have a sample of 5 numbers and I take the average. The estimator is taking the average of the sample. It is the estimator of the mean of the population. The average = 4 (for example), this is my estmate.
A representative sample is one where the statistics of the sample are the same as the statistics for the parent population.
That the key characteristics of the population are reflected in the sample.
sample is the population we make our study about them.
From a sample of a population, the properties of the population can be inferred.