According to stats, random sampling is important because it helps cancel out the effects of unobserved factors. for example, if you want to calculate the average height of people in a city and do your sampling in an elementary school, you are not going to get a good estimate. This is because the heights are conditional on a certain value of the unobserved factor “age”, so you do not have the unconditional mean. To make sure that specific levels of the hundreds of unobserved factors, like age, ethnicity, nutrition, gender, air quality, etc. are not conditionalizing your measurement of height, you have to make sure that your sample is collected in a way that on average, different levels of unobserved factors are represented. As a result, your measurements are not conditional on any specific level of any specific unobserved variable. The best way to do this is to use a random sample.

What problems/limitations could prevent a truly random sampling and how can they be prevented?

According to nedarc, a barrier to purely random samples is for some people in the population, you will find it difficult or impossible to locate them. For example, people who work unusual hours or who travel a lot may be selected to be included in the sample, but are not available when you attempt to contact them.

As I read about prevention of random sampling, every person in the population that is being observed should have an equal opportunity to be observed in the given study. The individuals are chosen from large or smaller demographics from which participants are randomly selected.  It will give the study the best possible results, because the participants include all characteristics within the targeted population