A sampling error represents a deviation in sampled audience versus the overall population. These errors are common - but there are ways we can avoid them.
A sampling error represents a deviation in sampled audience versus the overall population. These errors are common - but there are ways we can avoid them.
There is no such thing as a perfect, flawless survey. Sampling error are always going to represent a threat to the integrity and the validity of our research surveys; as such, it is vital that we remain aware of the different kinds of sampling error, and how they affect our data.
In this article, we will take a look at the most common types of sampling error, the ways we control our sampling error, and what reducing sampling error mean for the results of our market research surveys.
A sampling error occurs whenever there is a deviation in our sampled audience compared to the overall population being tested. In layman's terms, this means we’ve made a mistake somewhere during our surveying process - or that we’ve either over-represented or under-represented some variable in our test.
If we wanted to survey every person in America to find out what the most popular flavor of bubble-gum is, it could not be done with 100% accuracy in the results. Even if we hired pollsters in every city to count the results and assumed everyone answered honestly, there will be errors in the data we’re being given.
Although we can never perfectly extrapolate the results of our audience sampling to the greater population, there are ways that we can reduce the impact these sampling error have on the validity of our data.
Before we take a look at how we can reduce sampling error in our surveys, we must first familiarize ourselves with the most common forms of sampling error. Learning how sampling error can affect our data in different ways will allow us to gain a better understanding of how we can diminish their impact.
Whether you’re sampling random off people off the street - or you’re pulling from a targeted demographic segment over the internet - these errors are going to occur often in your surveys.
The reason we cannot avoid these errors is because we can almost never sample the entirety of a population; because this is true, we are always extrapolating our findings about our sampled audiences onto the greater population.
After we look at the most common forms of sampling error, we'll take a look at how we can use confidence levels and the standard deviation of our sample size to determine how closely our audience matches the true population.
Here are the most common types of sampling error that occur in research surveys, and how they occur throughout the research process:
Sampling error in its most basic form occur when there is a variation between the number of samples we measure in an audience vs. what the actual ratios look like in an overall population.
For an example of what a sampling error might look like, let’s assume we survey 100 baseball players about their batting stance. We want to know what the population of left-handed batters looks like compared to right-handed batters. We pick a sample size of 100 batters, and find out that 47 of them are left-handed; an impressive number, given that only about 10% of people on average are left-handed.
If we were to change our sample size to 1000 baseball players, we would find that our percentage of left-handed batters is much closer to 10%; this is because left-handed batters were over-represented in our initial sampling of batters.
We can control for sampling error in a number of ways.
A population specific error occurs when the researcher chooses the wrong audience to survey. Demographics are extremely important when it comes to surveys, as researchers are always looking to sample audiences that are as representative of the overall target population as they can be.
If a wine retailer decides to survey 100 teenagers about their average wine consumption habits, they would be creating a population specific error. More innocently, a sampling error might occur if the researcher has only a partially complete understanding of their audiences.
For example, we might see a manufacturer of cotton swabs interview audiences about their cotton swab consumption as it relates to hobby, and arts & crafts. This survey audience would be leaving out a key demographic of people who - against the manufacturer’s wishes (and we all do this) - use the swabs for ear-care and personal hygiene.
We control for population specification errors by conducting preliminary research of our audiences, and identifying our target demographics. Before we spend our marketing budgets researching untested audiences, we must first have a clear understanding of who our target markets are and what they value.
A selection error happens when pollsters opt-in to surveys themselves; the issue being that only respondents who are interested will participate. Because of this, our sampling will have left out what is likely the vast majority of potential respondents.
The US Census goes to great lengths to avoid selection error by motivating citizens to respond and by making the process extremely accessible. Not only can the census be completed online - the department employees citizens to go door to door throughout neighborhoods and communities.
To control for selection errors, we want to be prepping our audiences before the survey and following up with them after the survey is finished.
By initiating contact with our audiences early on in the surveying process, we can make more of an effort to drive their engagement with our survey. Offering survey incentives like raffles or coupons is one way we can promote a greater response rate in our surveys. Initiating surveys at the appropriate time is also crucial; for topics like user-experience surveys, retailers make sure to introduce shoppers to the survey immediately after a purchase has been made.
If your goal is to survey as wide and diverse a sample group as you can get, then you’ll need to make accommodations. Always give your audiences ample time to reply to surveys.
A sample frame error happens when researchers target an element of a population incorrectly during their sampling.
An example of error in sample framing occurs when researchers look at the number of guns in America. There are more guns in the United States than there are citizens; but this does not mean that every citizen owns one, let alone multiple guns. There are private collectors that own dozens or perhaps hundreds of firearms. There are also police forces and military bases where firearms are used for professional purposes, and are out of the hands of private citizens.
We can control for sample frame errors by conducting thorough research of our audiences beforehand. We will need to explore the uncontrolled variables that exist in our study, and attempt to frame our survey accordingly.
We can look at existing surveys and studies done by other researchers, and try to mimic their framing of the audiences we’re looking to sample.
A non-response error happens when the respondents that do participate in our survey differ greatly from those who do not.
In some cases, we may fail to consider a certain segment of our audience when disseminating our surveys. Other times, respondents refuse to participate for one reason or another. We cannot make our surveys compulsory, so it is up to us to try to be as effective as we can when capturing audience interest in responding to our survey.
We can apply the same practices we use to control for selection errors to manage the impact of non-response errors on our surveys.
Offering incentives and removing barriers that make it harder for participants from responding to your survey is the best way to motivate our audiences to engage with our surveys thoroughly and to bolster our survey sample size.
Sampling error represent an ever present threat to the validity of our survey results. By remaining aware of how different types of sampling error can occur in our work, we can design our surveys in such a way that promotes better data for our analysis.
If we have an understanding of what our sample size is and the standard deviation of our population, then we can calculate our sampling error.
The smaller the value of our sampling error, the closer our sample is to being truly representative of the actual total population. Researchers typically use a Z-score of 1.96 - a score that correlates to %95 confidence in the accuracy of our results.
Having a low sampling error means we are operating within a lower margin of error; thereby generating the results we need to gain a clearer understanding of the wants, needs, and desires of the entire population we're targeting.
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