Respuesta :
Answer: In random sampling, the whole population should have an equal chance of being chosen.
Step-by-step explanation:
In a simple random sample, every member of the population has an equal chance of being selected.
Your sampling frame should include the whole population. To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.
Biased sample
A sample is biased if individuals or groups from the population are not represented in the sample.
In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population have a lower or higher sampling probability than others.
Affects
It affects the internal validity of an analysis by leading to inaccurate estimation of relationships between variables. It also can affect the external validity of an analysis because the results from a biased sample may not generalize to the population.
Sampling is one of the procedure to get information about population. The best way to choose a sample representing a population is simple random sampling.
Why do we do sampling?
We do sampling so that we don't have to work on entire population of items but only on that sample in a way that we can predict information about population.
Most of the times, it isn't possible in real world to work on entire populations. Samples come out for rescue by assumptions that they will have some properties of the population pertained in them.
Thus, the sample should be taken out such that it includes data of the population as much as it can in unbiased way so as to imitate population. (assuming that we want the sample to contain the summary of the population).
What is simple random sampling?
If the sampling is done from the population randomly, without any other rule, then such sampling is called simple random sampling. Items are selected in a fixed size, all randomly.
What is stratified sampling?
Sampling which is done by taking the main population divided in categories(called strata), then such sampling from stratas (sub-parts of the population) is called stratified sampling.
What is systematic random sampling?
In this type of sampling, starting of the sampling is done randomly, but then rest of the samples are collected by an order. Like for example, first point is selected randomly, then all points are selected that they are equidistant from each other in the population.
What is convenience sampling?
The word "convenience" summarizes this sampling. It is done in a way that those values from population are sampled which are in convenience with the researcher. Like if some random picking is to be done from beads spread on the ground, then the researcher picking those beads which are closer to him.
Convenience sampling and stratified sampling are biased, as convenience sampling is leaving a good amount of population untouched, and stratified sampling is taking about sub-parts of the population.
The systematic random sampling is not good in those cases where population doesn't contain variation equally, and we might end up doing systematic sampling in that region which is biased than other regions.
The best sampling is simple random sampling, as it is randomly picking data values from the population, so is most probable to get the properties of the population(the more you're lost, the more probable you're to get to new paths). If sample size is large enough, this sampling comes out as best sampling method, in context of summarizing the information of the population, as almost all of the properties of the population is covered by values obtained during simple random sampling.
Thus, the best way to choose a sample representing a population is simple random sampling. (assuming that we want to summarize the population).
Learn more about simple random sampling here:
https://brainly.com/question/25644040
