Sample Size

Sampling - large and small samples. Trade-off between accuracy and cost

Exhibit 34.1   Sample size is a commercial decision that weighs the costs of a larger sample against the benefits of greater accuracy.

Sampling, by design, is imperfect and sample-based findings are never 100% accurate. However, they are meaningful and consequential if the study is well designed.

The determination of sample size involves practical and commercial considerations that weigh the costs of a larger sample against the benefits of greater accuracy (refer to illustration in Exhibit 34.1). There is not much value in the information sourced from a sample unless it can be generalised to the target population. The capacity to do so with some confidence depends on factors associated with sample design as well as non-sampling inaccuracies.

Small unreliable samples that do not permit generalization are not meaningful or useful. Conversely, large, overly accurate samples may be needlessly expensive. An ideal sample is one that precisely meets specifications — it is neither over specified nor underspecified. The determination of ideal sample size is dependent on the following factors:

  • Population variability. The greater the variability the larger the sample required to achieve the desired level of accuracy. In the case of continuous variables such as an item’s averages sales, this variability is best reflected by the relative standard deviation.
  • Sample design. For services like retail audits, a stratified sample design can yield a substantial reduction in sample size.
  • Specified level of accuracy. Specified level of accuracy. The standards for acceptable sampling error are set by the service provider. The higher the required precision, the larger the sample size.

Other factors specific to the nature of the research also affect sample size. For instance, for retail audits, larger sample of retail stores are required if products are thinly distributed. Similarly, for usage and attitude studies, if product usage is low, a larger sample of consumers will be required to achieve the desired level of accuracy.

It is pertinent to note that sample size is not dependent on universe size. This may sound counterintuitive — if universe size is not a factor, why then do we need large retail audit samples in big countries like China and India? The reason is that these markets exhibit greater variability and lower product distribution. Furthermore, large markets often have numerous market breakdowns, such as regions, provinces, and cities. Each market breakdown requires its own sample size to meet the specified accuracy standards, which increases the overall requirement.

When designing a sample, the level of data precision must be established based on the nature of the research objectives. Industry or agency norms often serve as guidelines for a wide range of market research programs, such as NielsenIQ’s sampling standards in retail measurement.

The level of precision is typically determined by specifying the standard deviation (standard error) of the parameter to be estimated or by stipulating the desired probability of achieving statistical significance for a particular estimate.

In addition to the precision levels, the calculation of sample size requires knowledge or assumptions about the market being studied. Some of this information is sourced from large-scale studies, such as household surveys or establishment surveys like the retail census for retail tracking studies. Other information, such as the proportion of the population using a brand, may be based on conservative assumptions.

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