Sampling is tricky

Selecting the right chunk of people who will give you the correct data is not an easy task. Unless you've picked the right set of people to answer your questions, you shouldn't expect to get the right answers.

It's important to evaluate and zero in on the exact audience you're going to survey before you start sending it out.

Targeting the wrong audience would mean getting incorrect data. If I ask questions about the most friendly dog breed to a bunch of cat owners, it would not make sense because I don't know if they are interested in dogs. Therefore, the answers I would get would be largely inaccurate. For this survey, the ideal thing is to have that domain knowledge about dogs, to comment on which one is the ideal breed. This handpicking of the right bunch of dog experts is called sampling.

So what does it take to select the right sample, and what are the different errors you must be careful to avoid? Let's start by defining some terms we need to understand.

There are several sampling techniques you can use to make sure that you're sending your survey to the right audience and getting the best results. It is important that researchers discuss this, take into account the limitations, and ensure transparency while selecting their sample.

The margin of error you see with survey results is actually an estimate of your sampling error.

As a surveyor, it's crucial that you carefully consider and then only select the samples that delivers the most accurate results. It's also important to be aware of other sampling errors that can lead to a flawed survey and to work hard to avoid them and you've already done Step 1 since you're here!


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