When the research objective is to compare a single group mean or frequency to a hypothetical / known value or proportion (such as an action standard or a norm), we have a choice among different statistical procedures, depending on the following variable characteristics:

Number of variables
One dependent variable

Examples:
1. Is our mean customer satisfaction score significantly different from the industry average (or action standard) of e.g. 4.6?
2. Is the 54/46 gender proportion in our sample significantly different from the population’s age proportions of 51/49?

When the dependent variable is BINOMIAL / BINARY / DICHOTOMOUS
1. One-sample Binomial test
2. Chi-square (χ²) goodness-of-fit test
3. G-Test

When the dependent variable is NOMINAL
When the dependent variable is ORDINAL / RANK-DATA
1. Wilcoxon signed rank test (also referred to as the “One sample median test”)

When the dependent variable is INTERVAL and passed the assumption of normality (parametric data)

When the dependent variable is INTERVAL but failed the assumption of normality (non-parametric data)
1. Wilcoxon signed rank test (also referred to as the “One sample median test”
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