Chi squared
Implies evidence of assocation, but doesn’t show how the variables are associated.
#inferentialstatistics l
This statement is typically referring to correlation. Correlation indicates that there is a relationship between two variables, but it does not reveal the nature of the relationship. In other words, it doesn’t show how changes in one variable directly influence the other. For example, a correlation might show that as one variable increases, the other tends to increase as well, but it won’t explain why this is happening or if there are any other factors involved in the observed association.
Give me a table with examples:
| Variable A | Variable B | Correlation |
|---|---|---|
| Hours spent studying | Test scores | A positive correlation is often observed, suggesting that the more hours a student spends studying, the higher their test scores are likely to be. However, this correlation doesn’t explain how effective the study methods are, or if there are other factors at play such as a student’s innate ability, mental health, etc. |
| Age | Income level | There is typically a positive correlation between age and income level. As people get older and gain more experience in their fields, they tend to earn more money. But this doesn’t explain other factors like education level, industry of work, location etc., that might also influence an individual’s income. |
| Outdoor temperature | Ice cream sales | There is usually a positive correlation showing that as outdoor temperatures increase, so do ice cream sales. However this doesn’t explain why people buy more ice cream when it’s hot or if there are any other factors involved like promotional offers during summers etc. |
| Exercise amount | Body weight | Generally there is a negative correlation implying that as exercise amount increases, body weight decreases. But it doesn’t show how these variables are associated i.e., it doesn’t take into account diet habits or genetic factors that could also affect body weight. |