people who arrive early versus people who arrive late.
Imagine the experimenter instead uses a coin flip to randomly assign participants.
At the end of the experiment, the experimenter finds differences between the Experimental group and the Control group, and claims these differences are a result of the experimental procedure.
However, they also may be due to some other preexisting attribute of the participants, e.g.
To express this same idea statistically - If a randomly assigned group is compared to the mean it may be discovered that they differ, even though they were assigned from the same group.
If a test of statistical significance is applied to randomly assigned groups to test the difference between sample means against the null hypothesis that they are equal to the same population mean (i.e., population mean of differences = 0), given the probability distribution, the null hypothesis will sometimes be "rejected," that is, deemed not plausible.Random assignment of participants helps to ensure that any differences between and within the groups are not systematic at the outset of the experiment.Thus, any differences between groups recorded at the end of the experiment can be more confidently attributed to the experimental procedures or treatment.You want to make sure your sample is randomly selected (hence, a random sample) to make sure that everyone in your sampling frame has an equal chance of being selected.You don’t want to just select a “convenience sample,” the last 20 people who ordered from you, the last 20 customers when they’re listed alphabetically, etc. If you sample the last 20 customers for example, they may be your newest customers who are only familiar with your most recent products or website design.Because most basic statistical tests require the hypothesis of an independent randomly sampled population, random assignment is the desired assignment method because it provides control for all attributes of the members of the samples—in contrast to matching on only one or more variables—and provides the mathematical basis for estimating the likelihood of group equivalence for characteristics one is interested in, both for pretreatment checks on equivalence and the evaluation of post treatment results using inferential statistics.More advanced statistical modeling can be used to adapt the inference to the sampling method.Once you have your sampling frame (potential survey respondents) in Excel, you can easily select a random sample of them.For example, if you have 3,000 customers and you would like to select a random sample of 500 to receive a customer satisfaction survey, follow these steps: To make sure the number of respondents in your random sample are statistically significant, check out this blog post.By generating a random sample, you’re minimizing the bias that results from picking an convenience sample from your sampling frame.This can sound daunting, but you don’t actually need to be a statistician or mathlete to do this. Just put your sampling frame—the customers you have contact info for—into your spreadsheet.