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Every statistical method has assumptions. Assumptions mean that your data must satisfy certain properties in order for statistical method results to be accurate. Continuous means that the variable can take on any reasonable value. Some good examples of continuous variables include age, weight, height, test scores, survey scores, yearly salary, etc. The variable that you care about must be spread out in a normal way.
In statistics, this is called being normally distributed aka it must look like a bell curve when you graph the data. The data points for each group in your analysis must have come from a simple random sample. This means that if you wanted to see if drinking sugary soda makes you gain weight, you would need to randomly select a group of soda drinkers for your soda drinker group.
The key here is that the data points for each group were randomly selected. This is important because if your groups were not randomly determined then your analysis will be incorrect. In statistical terms this is called bias, or a tendency to have incorrect results because of bad data. If you do not have a random sample, the conclusions you can draw from your results are limited.
You should try to get a simple random sample. The sample size or data set size should be greater than 5 in each group. Some people argue for more, but more than 5 is probably sufficient. The sample size also depends on the expected size of the difference across groups. When would you use a factorial Anova? What is a factorial Anova test used for? What is an example of a factorial Anova?
When would a researcher choose to use a factorial Anova instead of a one way Anova? When would you use a different Anova? What is the difference between one-way and factorial Anova? What is the null hypothesis for factorial Anova? What does the F value tell you in Anova? Why would you use the Tukey Kramer procedure? Why do a Tukey post hoc test? What is a Tukey comparison? When would you use Tukey Kramer test?
When would you use the Tukey Kramer procedure quizlet? What does post hoc mean? She plants seeds and lets them grow for three months under different conditions for sunlight exposure and watering frequency. After three months, she records the height of each plant. She could use a factorial ANOVA for this analysis because she wants to understand how two factors affect a single response variable. A professor wants to understand how class time and teaching method affect exam scores. He uses two different teaching methods and two different teaching times early morning and early afternoon and records the average exam scores of each student at the end of the semester.
He could use a factorial ANOVA for this analysis because he wants to understand how two factors affect a single response variable. An economist collects data to understand how education level high school diploma, college degree, graduate degree , marital status single, divorced, married , and region North, East, South, West affect annual income. He could use a factorial ANOVA for this analysis because he wants to understand how three factors affect a single response variable.
A botanist wants to know if sunlight exposure and watering frequency affect plant growth. She plants 40 seeds and lets them grow for two months under different conditions for sunlight exposure and watering frequency. After two months, she records the height of each plant. We can see that five plants were grown under each combination of conditions. For example, there were five plants grown with daily watering and no sunlight and their heights after two months were 4.
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