<a href=https://explorable.com/population-sampling>Population Sampling Techniques</a>. Population sampling is the process of taking a subset of subjects that is representative of the entire population. The sample must have sufficient size to warrant statistical analysis.
https://explorable.com/population-sampling
The advantage of using probability sampling is the accuracy of the statistical methods after the experiment. It can also be used to estimate the population parameters since it is representative of the entire population. It is also a reliable method to eliminate sampling bias.
https://explorable.com/population-sampling
In probability sampling, every individual in the population have equal chance of being selected as a subject for the research. This method guarantees that the selection process is completely randomized and without bias.
https://explorable.com/population-sampling
Professional researchers typically set a sample size level of about 500 to optimally estimate a single population parameter (e.g., the proportion of likely voters who will vote for a particular candidate). This will construct a 95% confidence interval with a Margin of Error of about 4.4% (for large populations). Since there is an inverse relationship between sample size and the Margin of Error, smaller sample sizes will yield larger Margins of Error.
http://www.research-advisors.com/tools/SampleSize.htm
These respondents are known as the sample and are representative of the general population or universe. A sample design is a definite plan for obtaining a sample from a population. It refers to the technique or the procedure for obtaining a sample from a given population. Following are the characteristics of good sample design: 1. Sample design should be a representative sample: A researcher selects a relatively small number for a sample from an entire population. This sample needs to closely match all the characteristics of the entire population.
http://blog.reseapro.com/2012/12/characteristics-of-a-good-sample-design/
For example, a sample size of only 100 will construct a 95% confidence interval with a Margin of Error of almost 13%, too large a range for estimating the true population proportion with any accuracy.
http://www.research-advisors.com/tools/SampleSize.htm
To use these values, simply determine the size of the population down the left most column (use the next highest value if your exact population size is not listed). The value in the next column is the sample size that is required to generate a Margin of Error of  5% for any population proportion.
http://www.research-advisors.com/tools/SampleSize.htm
If the sample used in an experiment is a representative sample then it will help generalize the results from a small group to large universe being studied. 2. Sample design should have small sampling error: Sampling error is the error caused by taking a small sample instead of the whole population for study.
http://blog.reseapro.com/2012/12/characteristics-of-a-good-sample-design/
How sure do you need to be that the answers reflect the views of your population? This is your margin of error. So if, for example, 90% of your sample likes grape bubble gum. A 5% margin of error would add 5% on either side of that number, meaning that actually 85-95% of your sample likes grape bubble gum.
https://www.surveymonkey.com/mp/sample-size/
In this type of population sampling, members of the population do not have equal chance of being selected. Due to this, it is not safe to assume that the sample fully represents the target population. It is also possible that the researcher deliberately chose the individuals that will participate in the study.
https://explorable.com/population-sampling
what is a good sample size for a population
500 is a good sample size for a population. A good sample size for a population is five hundred.