Welcome back, there are many ways to sample. There are entire courses and books dedicated to this topic. In this lesson, let me address some of the ways of sampling that are prevalent. After this lesson, you'll be able to decide how many people you need to sample for your research project and which kinds of sampling you will use. Let's start out with figuring out your sampling size. Technically, you should base the size of the sample on the need for credibility and the risk level involved. There are websites you can use to determine error margin calculations. But first, you've gotta look at that purpose. You may have heard before that you need at least 400 people for an internet survey. How do people come up with the number that is tossed around quite a bit. Here's how. Market research commonly aims to get an error margin of plus or minus 5% at 95% confidence. That is generally the industry standard across the board. In order for you to do that, you need to have something like 387 but people always say 400. That's why you often hear 400 for a sample size. That's a general sample size that you need to have in order to be scientifically credible among the profession. However, people often sample fewer, say 50 or 100 and they accept higher levels of error. But the risk may not be as bad. There are other cases where people will survey 1,000 or 5,000. That may be to reduce the error margin, but it's also to increase public perception. Every news outlet that does a pole never reports 400 because 400 is not a cool number. 1,000 is what they report, they did 1,000 surveys of registered voters or a sample size of 5,000 registered voters. Nobody announces 400 on national television. When you survey 1,000, you have an error margin of plus or minus 3%. It's very unlikely that you're going to be wrong. There's still that 3%, but your chances of being wrong are less. It's also considered newsworthy to have 1,000 or 5,000 for the media. So aim for a 5% error margin or less to be considered credible. There are many margin of error calculators or sample size calculators. The one I use is from the American Research group. But there are many others including ones from SurveyMonkey, Qualtrics, or Raosoft. Using such calculator, you can insert a sample size of say 400 and this assumes a very large population and it'll produce a error margin of plus or minus 3% and 95% confidence. And this assumes a population of the U.S. or large populations in California or Pennsylvania. However, when you have a customer base of, say, 643, you're sample size doesn't have to be 400. It's going to be something different. If I were to use a sample size calculator and plugged in that I had a population of 643 customers and I want a plus or minus 5% I would run the calculation, and a calculator would tell me to do 240 surveys of my 643 to get plus or minus 5%. The American research group that provided the calculator I use is a company that does surveys and survey research. This is just one of their tools to get you onto their site. You can also do the same thing with SurveyMonkey or contracts who also have sample such calculators. Now let's consider sampling,what kind of sampling do you use from the many available. A common tool for most market researchers is the random sample. This is dine in a manner to exclude any bias or systematic errors in selecting a sample. It's typically the best way to sample, but also more rigid. This approach has a higher likelihood to get market research results that represent the population. However, there are other sampling methods as well, such as a convenience sample, where the researcher gets the first x number of people they need to address a situation. There are clear disadvantages to this, but the major advantage of this approach is time. Most people who employ a convenient sample do so in a low risk situation. There are other sampling methods like a quota sample where you're done when you reach a certain numbers of quota. There's also a snowball sample where you build your sample based on your existing sample or participants. The ladder is often acceptable approach when you're having difficulty finding qualified research participants. Some people use this approach for in-depth or executive research sometimes called key informant or an opinion leader research. There are many other ways of sample and entire courses are books dedicated the sampling, explore the subject in much greater detail. A random sample is the preferred method of sampling, however, there are many corporations, companies, organizations in market research firms that use other sampling methods. It really comes down to what their constraints are and what their goals are. If their goal is to get quick information, such as talking pandas versus talking rabbits, they may not need a random sample for that, they may just need a tie breaker. In this case they might just want to do a quota sample. Let me explain quota sampling. That was the example I gave earlier on the talking pandas and talking rabbits situation. In that the researcher needed to get the first 50 people they can get from a target age audience of those under 25. A random sample, you actually have to say, okay, there's ways to create randomness in terms of how to find these first 50 people. You take a random number and you take that 17th person and then you do another random number and you take the 212th person. You use a random number generator, which uses some kind of algorithm that gives you random numbers to choose emails or phone numbers. For the quota sample, there's no randomness about it. The quota sample and a convenience sample are actually a lot alike. It's all about ways to get to that number you need for your sample size very quickly. A snowball sample, on the other hand, is used when you have very few participants and that are hard to reach participants. For example, you might have difficulty finding people with osteoporosis. You want to test market a certain marketing message to them. However, people that have osteoporosis might be in a support group where they know other people with osteoporosis. So after you survey them, you ask them. Okay, is there anybody else with this condition that I could actually tap into? You can ask them, how can I contact your friend? And how can I contact your other friend? And you snowball off these people. It's a very highly biased method of sampling. But it's fairly efficient in terms of reaching a hard to reach audience. This actually happens a lot with business surveys as well. You may need to do an in-depth interview with a business leader in terms of hiring millennials, but it's a business leader in the engineering field. So you're talking about engineering being hard to reach. You might say something like, okay, who will be your peer at another company, and they'd say okay, talk to Jansen Smith in XYZ corporation. And so now you're interviewing her, and then you go on to interview two of Jansen Smith's contexts. In the snowball sample you're very very bias, but at the same you're getting data on a very targeted or niche market.
