when pulling together a sample consider getting assistance from a colleague or an independent person they may be able to identify biases or discrepancies of which you may not be aware 
simple random vs other sampling methodsa simple random sample is used to represent the entire data population a stratified random sample divides the population into smaller groups known as strata based on shared characteristics unlike simple random samples stratified random samples are used with populations that can be easily broken into different subgroups or subsets these groups are based on certain criteria then elements from each are randomly chosen in proportion to the group s size versus the population in our example above s p 500 companies could have subsets defined by type of industry or geographical region of the company s headquarters this method of sampling means there will be selections from each different group the size of which is based on its proportion to the entire population researchers must ensure that the strata do not overlap every point in the population must only belong to one stratum because they should be mutually exclusive overlapping strata would increase the likelihood that some data are included thus skewing the sample systematic sampling entails selecting a single random variable that determines the internal how the population items are selected for example if the number 37 was chosen the 37th company on the list sorted by last name of the ceo would be selected by the sample then the 74th i e the next 37th and the 111st i e the next 37th after that would be added as well simple random sampling does not have a starting point therefore there is the risk that the population items selected at random may cluster in our example there may be an abundance of ceos with a last name that starts with the letter f systematic sampling strives to even further reduce bias by ensuring that these clusters do not happen cluster sampling can occur as a one stage or two stage cluster in the former items within a population are put into comparable groupings using our example companies are grouped by year formed then sampling occurs within these clusters two stage cluster sampling occurs when clusters are formed through random selection the population is not clustered with other similar items sample items are then randomly selected within each cluster simple random sampling does not cluster any population sets though it may be a simpler clustering especially two stage clustering can enhance the randomness of sample items in addition cluster sampling may provide a deeper analysis on a specific snapshot of a population which may or may not enhance the analysis advantages and disadvantages of simple random sampleswhile simple random samples are easy to use they do come with key disadvantages that can render the data useless ease of use represents the biggest advantage of simple random sampling unlike more complicated sampling methods such as stratified random sampling and probability sampling no need exists to divide the population into subpopulations or take any other additional steps before selecting members of the population at random a simple random sample is meant to be an unbiased representation of a group it is considered a fair way to select a sample from a larger population as every member of the population has an equal chance of getting selected therefore it has less chance of sampling bias a sampling error can occur with a simple random sample if the sample does not end up accurately reflecting the population it is supposed to represent for example in a simple random sample of 25 employees it would be possible to draw 25 men even if the population consisted of 125 women 125 men and 125 nonbinary people for this reason simple random sampling is more commonly used when the researcher knows little about the population if the researcher knows more it would be better to use a different sampling technique such as stratified random sampling which helps to account for the differences within the population such as age race or gender other disadvantages include the fact that for sampling from large populations the process can be time consuming and costly compared with other methods researchers may find that a project not worth the endeavor of its cost benefit analysis does not generate positive results as every unit has to be assigned an identifying or sequential number prior to the selection process this task may be difficult based on the method of data collection or size of the data set each item within a population has an equal chance of being selected there is less of a chance of sampling bias as every item is randomly selected it is easy and convenient for data sets already listed or digitally stored incomplete population demographics may exclude certain groups from being sampled random selection means the sample may not be truly representative of the population depending on the data set size and format random sampling may be a time intensive process 