Introduction > Step-by-Step Statistics > Now You've Mastered the Basics > What Analysis Should I Use?

What Analysis Should I Use? The key to deciding what statistical analysis you should use lies in the identification of your variables and the classification of them as either continuous or discrete. Continuous data consists of observations or values that can meet any value between two points, such as height, weight, temperature or pH. Discrete data consists of observations or values that fall into distinct categories and cannot fall between any two points. For example, if you were investigating the effects of different pesticide active ingredients (pyrethrum, diazinon and endosulfan) on the number of whitefly eggs produced, the explanatory variables are pyrethrum, diazinon and endosulfan. They are categorized as discrete because they fall into distinct categories. The response variable is a number of eggs, which are classified as discrete because you’d be counting whole numbers eg 1, 20, 34 etc and it’s not possible to record fractions of eggs. Use the following data to identify the statistical analysis you should use, based on your explanatory and response variables.   Continuous explanatory Discrete explanatory Continuous response Regression ANOVA or T-test Discrete response Regression Chi-Squared Exercise Can you differentiate between continuous and discrete variables, and determine what statistical analyses should be conducted?

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