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The different levels limit which descriptive statistics you can use to get an overall summary of your data, and which type of inferential statistics you can perform on your data to support or refute your hypothesis. The level at which you measure a variable determines how you can analyze your data. In ratio scales, zero does mean an absolute lack of the variable.įor example, in the Kelvin temperature scale, there are no negative degrees of temperature – zero means an absolute lack of thermal energy. You can categorize, rank, and infer equal intervals between neighboring data points, and there is a true zero point.Ī true zero means there is an absence of the variable of interest. A zero on a test is arbitrary it does not mean that the test-taker has an absolute lack of the trait being measured. The same is true for test scores and personality inventories. But zero degrees is defined differently depending on the scale – it doesn’t mean an absolute absence of temperature. The difference between any two adjacent temperatures is the same: one degree. You can categorize, rank, and infer equal intervals between neighboring data points, but there is no true zero point.