Are these discussions well explained or not, and why? Provide examples

1-After reviewing the Spurious Correlations examples, the graphs made me wonder, who has the time to put those concepts together at first look? As I examined them again, I realized that these graphs were extremely interesting. I had not realized that just so many people drowned accidentally every year but looking at the other side of the graph I realized that the numbers were extremely low. The movies went up in a multiple of two and the drowning rates went up by multiples of 20. At first look the rates matched each other but, the two objects seem to just be correlated by the shape of the graph and the years. This way you understand that they are correlated but these two things do not cause each other to happen. When you look at a graph quickly you might think that they are causation graphs, but they are not.

2-When interpreting spurious correlation amounts between two factors or variables that seem related but are not and are infiltrated by a third factor. This third factor could affect the two initial variables, which causes misleading correlations of spurious relationships. Some things to consider or look out for:

Ensuring a proper representative sample

Obtaining an adequate sample size

Being wary of arbitrary endpoints

Controlling for as many outside variables as possible

Using a null hypothesis and checking for a strong p-value

To find causation, factual evidence from a study shows a causal relationship between two variables.

3-Years ago I heard the phrase "correlation does not mean causation" and was given the example of increased shark bites along with increased ice cream sales. From that point on I always second-guessed correlations that were meant to show relationships between items. A big reason I want to enter the field of research is because I still believe there are many cases where a missing factor has not yet been identified. I would like to critically analyze data and find missing factors or build upon the known.