Generally, research practitioners utilize the following sequence and inputs in computing sample size:

1. Survey respondents will split 50/50 in response to dichotomous (e.g. yes/no) questions.

2. The desired level of confidence will be 95%, or 1.96 standard deviations from the mean or .05 possible .

Py = Proportion responding “yes”

Pn = Proportion responding “no”

 Standard error is the acceptable amount of error/confidence interval. In the above case .05/1.96 (about 2 standard deviations), or .0255102. The standard formula for computing the sample size is:

Py) (Pn)

So, when the respective values are input, we end up with .25/.0006507 or 384 respondents. This is why a survey sample size of 400 is often recommended.Sample size is important in avoiding Type I or Type II errors.

Type I errors  are made by stating that there is a difference between two groups within a population on a given measurement, when in fact there is no difference. Accommodating this potential outcome is where most sample size calculations stop. Often, practitioners simply ignore the possibility of making a Type II error. The sample size typically needed to address Type I errors is 384.

Type II errors  are made by stating that there is no difference between two groups within a population on a given measurement, when in fact there is a difference. While important, many researchers ignore statistical power calculations. In the “real world” tables and canned statistical tools are utilized to determine survey power, due to the complexity of the formulas. The sample size typically needed to address Type II errors is 1,236.

Confidence level   suggests that other samples drawn from the same population will have similar values X% of the time. For most marketing research exercises, confidence levels are set at 95%.

Confidence interval   includes the possible end point values for the entire population. The confidence interval allows for a computed amount of variation from the mean value based on the precision/cost value trade-off.