The first question I always get from clients interested in conducting a survey is about sample size. Many confuse sample size with representativeness. They are related, but not the same, particularly if convenience samples are used.

In random samples, as we increase sample size the chance each member of the target population has of being selected increases and consequently more segments of the population are likely to be represented. This is based on the assumption that we have a list with all the population members (population frame) and know their probability of being chosen. This could be the case of a customer database/list, if that’s our population of interest. 

In convenience samples, the population frame becomes the pool of individuals in the sample source (e.g. online panels), which may not include all segments in the target population or only have a few members of certain segments, depending on how the sample source is built. In this case sample quotas, weighting schemes, and mixed mode data collection methods (online/phone/intercepts) are often used in an effort to reach representativeness. 

Assuming that we are able to pull a representative sample of the target population by whatever affordable means are available to us, we need to give serious consideration to sample size. This is a case where size matters (pun intended). Why?

It is all about precision, tolerance for risk and cost. For samples smaller than 1000, we always have to think about how confident we want to be that estimates are within a particular range (level of confidence and risk), and how small we want that range to be (level of precision). Unfortunately, they go in opposite directions. Higher levels of confidence require greater ranges (margins of error) in small sample sizes.

For instance, we can be 95% confident that the true estimate for a variable in a sample of 400 is within +/-4.9%, however, if we want a smaller margin of error, in an attempt to gain more precision, with the same sample size, we have to sacrifice certainty and may need to accept a 90% confidence level to get a +/-4.1% margin of error. At the 95% confidence level you are more certain, but less precise as you expand the range to make sure the true value falls in it. At the 90%, you are more precise, but less certain.

If you want to get more precise estimates without sacrificing certainty in the results, then you have to increase sample size, which in turn increases research costs. As the table below shows, as sample increases the differences in margin of error across the different confidence intervals become smaller.

At the end of the day, when it comes to sample size, you need to decide what it is more important to you, certainty or precision, and what your tolerance for risk is, especially if your market research budget is small.

For more help on calculating sample size and margin of error, use our Sample Size and Margin of Error Calculators.

I meet many clients who worry about sample size  trying to ensure they get an enough large sample so that statistically significant differences can be found and inferences to a larger population can be made, but they often don’t know that these statistical tests were meant to work within the probability sampling theory framework.

Since the advent of online panels and the increase of online surveys using panel-provided samples, the issue of testing for significant differences using standard parametric tests has become a moot point in many research studies.

Nowadays many of the surveys conducted online use samples provided by online panels, but these are mostly convenience samples (non-probability). The populations of online panels include respondents who are willing to participate in studies, excluding those unwilling to be part of the panel who may be members of the target population we are after.

In probability sampling, each possible respondent from the target population has a known probability to be chosen. Probability sampling helps us to avoid some of the selection biases that can make a sample not representative of the target population. For more on this read Does A Large Sample Size Guarantee A Representative Sample?

A single probability sample doesn’t guarantee to be representative of a target population, but we can quantify how often samples will meet some criterion of representativeness. This is the notion behind confidence intervals. The probability sampling procedure guarantees that each unit in the population of interest could appear in the sample.

By taking into account all possible random samples that can be taken from a population, we can estimate how often the true value of an estimate can be expected to be within a specific range of values. So, when we  talk about a 95% confidence interval, this really means that the true value of a particular variable is expected to fall within an interval of values 95  out of 100 times we repeat the procedure. When an opinion poll indicates that 50% of people are in favor of a political decision with a +/-3% margin of error at a 95% confidence interval, it is really saying that we can expect that between 47% and 53% of people will be in favor of the decision 95 out 100 times, if we were to repeat the poll. When we test for significant differences, we are looking to see if the value falls outside that range.

Unfortunately, taking a probability sample is hard and costly. For most consumer research studies and social behavior studies, we really don’t know the size of the actual population of consumers behaving in certain ways or consuming certain products, and trying to find out would make the research prohibitively expensive. This is why we often have to settle for convenience samples like the ones offered by online panels. They still can offer valuable insights if designed with care, but again doing statistical testing in a convenience sample is pointless since the assumptions about probability sampling are violated.

Online panels are here to stay, and they will continue to be a source for affordable sample for market research. Research using convenience sample is often better than not research at all if the survey is well designed and screening criteria are used to define the target population.

A more appropriate case for testing statistically significant differences are random samples taken from a customer database, since this is essentially the population frame where we can count all members and estimate their probability to be chosen.

 However, if you don’t have a customer database or are interested in surveying non-customers, then  use a convenience sample, if that is what your research budget can afford or there is no other way to get to the actual population frame (list to pull the sample from), but don’t fret about testing for significant differences. You may feel more confidence if you are able to replicate the results in repeated surveys, but be always cautious about inferences made from convenience samples since there could be a hidden systematic bias in the data.

It is always important that whenever you use convenience samples  you consider the following when analyzing the results:

         1. Who is systematically excluded from the sample?

         2. What groups are over- or underrepresented in the sample?

         3. Have the results been replicated with different samples and data collection methods?

If testing for significant difference gives you peace of mind, even when using convenience samples, do it to confirm the “direction” of the data, but restrain yourself from doing inferences to a larger population.

To learn more about our consumer data service visit Consumer Shopping Behavior Insights. To request consumer shopping behavior data and insights don’t hesitate to contact us.