Sampling and bias
Different kinds of sampling and bias in sampling
No matter what kind of study we’re doing (observational or experimental), we always want to make sure that the subjects in our study are picked randomly if we’re using a sample.
Remember that a population ???N??? always consists of the entire group of subjects we’re interested in. A sample ???n???, on the other hand, is a smaller group within the larger population.
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So if we’re able to study the entire population, then we don’t have to worry about the group we look at, because we’re looking at everybody, or everything. But if we’re only going to be using a sample to represent a larger population, then how we pick the subjects that will be in our sample is very important.
In a perfect world, we want the sample to be representative of the population. If it were in fact perfectly representative, we might call it a representative sample, because the information we collect about the sample would “scale up” to the population.
For example, we might want to know how many strawberries are in our fruit salad. The fruit salad fills a ???20???-cup bowl, so we don’t want to pick through the entire thing and count every strawberry. Instead, we’ll take a ???1???-cup scoop, and hope that it’s a representative sample of the entire bowl.
In the ???1???-cup scoop, we count ???2??? strawberries. If the sample (the cup) perfectly represents the population (the entire bowl), we could “scale up” the strawberry count for ???1??? cup to ???20??? cups to get a count of the number of strawberries in the entire bowl.
???\frac{2\text{ strawberries in our sample}}{1\text{ cup in our sample}}=\frac{x\text{ strawberries in the bowl}}{20\text{ cups in the bowl}}???
???\frac21=\frac{x}{20}???
???2=\frac{x}{20}???
???40=x???
Therefore, based on the sample, we guess that there are ???40??? strawberries in the whole bowl. If there are in fact ???40??? strawberries in the whole bowl, then the single ???1???-cup scoop would have been a perfectly representative sample of the entire fruit salad.
But this won’t always be the case. Maybe the fruit salad wasn’t perfectly mixed, and a lot of the strawberries had sunk to the bottom. If we knew that there were actually ???100??? strawberries in the bowl, then the sample we picked wasn’t a very good one.
Many times a sample won’t do a good job representing the population because of bias.
Bias in sampling
Bias, by definition, is showing favor toward something over something else. When we talk about bias in statistics, we’re basically talking about something that skews our results and makes them inaccurate.
When we collect data for a sample, and we’re using that sample to represent the population, we mentioned before that we want a representative sample. We also call this an unbiased sample.
To get a representative, or unbiased, sample, we try to avoid introducing bias into the data. Unfortunately, it’s really easy to introduce all different kinds of bias into a data set and skew our results:
Response bias
Measurement bias: There’s something wrong with the tool we’re using to collect the data, so our method of collecting observations or responses from the sample results in false values. For example, if we calibrated a scale improperly before taking measurements, then all the results would suffer from measurement bias.
Response bias: People don’t always want to answer questions truthfully. If our survey question asks “Have you ever stolen something?” people may not answer truthfully. If anyone participating in our survey lies when they answer this question, then we have some response bias in our data. This is similar to measurement bias because in both measurement and response bias, there’s something wrong with the tool we’re using to collect data.
Biased wording: Biased wording is when we word a question in a way that makes people want to answer in a certain way. It’s often the cause of response bias in surveys, and sometimes people who write surveys intentionally use biased wording to sway the results one way or another. For example, if our survey question asks “Have you ever rudely interrupted someone else when they were talking?” people may be offended by the word “rudely.” Instead of simply asking whether or not they’d ever interrupted someone, we put a value judgement on the question by inserting “rudely” into the question. This biased wording might influence the way they answer.
Undercoverage
Selection bias: Also called undercoverage, this is when we don’t collect data from an entire group of subjects that should have been included in our data. For example, let’s say I own a daycare center and want to find out the mean household income of the families whose children I look after. If I watch ???20??? children, and I choose to sample only the parents who pick up their kids before ???5:00??? p.m., I might be dramatically skewing my data. What if the parents who work later have significantly higher incomes, because anyone who picks up their child before ???5:00??? only works part time? I’m not representing all the parents who work late, so that part of the population is under-represented.
Voluntary response sampling: This is when people voluntarily respond to my survey or participate in my study, which means that voluntary response sampling can be a cause of selection bias. People who voluntarily participate may have different habits, tendencies, opinions, or backgrounds than people who tend not to participate. So the data we collect from a sample of voluntary respondents may be biased.
Convenience sampling: This is when we choose a sample simply because it’s convenient, not because we’re trying to get a good, random representative sample. Therefore, this can be another cause of selection bias. There’s almost always some aspect of convenience to sampling, but a good example would be if we’re trying to collect data about the people in our city, and we just ask the neighbors who live on our street. It’s really convenient to collect data for our street only, but it certainly doesn’t give us an unbiased sample for the entire city, so this convenience sample may cause a big problem.
Non-response bias
Non-response bias: This is when you get a large number of people who don’t respond to your survey. There may be bias in your data because you didn’t collect answers from everyone who didn’t respond, and you don’t know what they might have said. For example, state representatives often send surveys to all of their constituents to ask them how much they care about different political issues. If they only get a response rate of ???5\%???, that means ???95\%??? of constituents didn’t bother to send back the survey. Which means the representative only collected opinions from ???5\%??? of the population they were interested in, so nonresponse bias may be a big issue.
Direction of bias
Based on the bias that we suspect may exist in our sample (response bias, undercoverage, and non-response bias), we always want to be able to make an educated guess about whether our results are more likely producing an overestimate or an underestimate.
For example, in the response bias section, we talked about the survey question “Have you ever stolen something?” If that question exists in our survey, it could actually lead to response bias or undercoverage, depending on whether people choose to skip the question or lie.
Or in the undercoverage section, we talked about surveying only the parents who pick up their children from daycare before ???5:00??? p.m. We might admit that we have some undercoverage in our data, and would guess that our estimate for household income is low, since parents who work late might make more money than those who get off work earlier.
Either way, we always want to be thinking about the kind of bias we’re introducing, and whether the bias in our data has caused us to overestimate or underestimate the value we’re looking at.
Sampling techniques
So how can we avoid bias in our sample data? Well, there are few techniques we can use to divide subjects into groups that help ensure that our data stays as random (unbiased) as possible.
First, we could assign a number to each subject in the population, then pick numbers out of a hat, assigning every other subject to the same group. Or, we could use a random number generator on a computer to do the same thing. We could also get a computer generated string of digits, and then pick out numbers in order from the random number string.
When we assign subjects to groups in a totally random way like this, we call it a simple random sample. But even if we assign subjects to groups in a totally random way, we can still end up with a skewed sample. For example, it’s possible that we could use a random number generator on a computer to randomly put ???50??? men and ???50??? women into two different groups, and yet end up with ???40??? men and ???10??? women in one group, and ???10??? men and ???40??? women in another group.
To fix problems like this, instead of doing a simple random sample, we can try to take a stratified random sample, where we put some parameter on the sample where we require an even number of subjects from different groups. For example, if we want to have one group of ???25??? men and one group of ???25??? women in our sample, then we’ll treat men as one population and women as another. Then we’ll take a random sample of ???25??? men from the male population and a random sample of ???25??? women from the female population. These two groups (men and women) are called the strata of the stratified random sample.
Earlier we used the term blocking to describe this. These concepts are the same thing. The strata in sampling are what we called the blocks in an experiment with a randomized block design.
We could also take a clustered random sample, where we break our population into clusters, and then either 1) take a random sample within each cluster to be our total sample, or 2) randomly pick some clusters and then sample everyone in those clusters.
In a cluster sample you want each cluster to be similar to the population as a whole. For example, say you have a nicely mixed fruit salad that’s been divided into ???12??? portions. Then each of these portions is a cluster. Once the population is divided into representative clusters you can take a simple random sample of clusters, say ???3??? out of the ???12??? portions to analyze the sample.
In the case of a stratified sample, the fruit salad would’ve had to be separated back into strawberries, bananas, watermelon etc., and a sample from each group selected. So often you will hear a stratified sample is the same within groups, (like each fruit is the same) and a cluster sample is the same between groups, (like each portion of fruit salad should be representatively the same).
