Question 3: Given the data, is the conclusion drawn a logical one?

1) Strength of association (how strong is the association between the data?) 2) Consistency (will the same results be found if different people replicate the study at different times?) 3) Specificity (how specific is the association?) 4) Temporality (did the effect occur after the cause?) 5) Biological gradient (is there a correlation between the a

Clusters are exactly what they sound like: where notable groups of data fall.

This chapter will look at five common pitfalls in statistics and how you, the average consumer, can recognize them.

the sample must be representative of the entire population, meaning it must share the same characteristics of it in the same proportions.

Pitfall #2: Looking at the wrong measure of center

Another way of thinking about it is that p-values tell us how likely it is that another experiment would have the same results.

In this last step, you might take the findings from a sample and apply them, drawing inferences and conclusions about the larger population.

Pitfall #5: Getting causation backwards