Part I
Correlation means correlation. Statistically, it measures the extent of the relation between two variables. A correlation will be positive if one increase in the variable is related to an increase in the other variable. On the other, a negative correlation is whereby an increase in one variable is associated with a decrease of the other variable. It is established when describing the relation between dependent and independent variables.
In general, there a difference between correlation and causation (cause and effect). More often, in correlation, the dependent variable has to change because the independent variable has changed. However, on the causation, the two variables (cause and effect) remains constant but the occurrence of one factor (cause) allows the occurrence of the other factor (effect). For instance, diet and cancer demonstrate cause and effect claim. It is because of certain diet that people suffer from cancer.
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Correlation claims mean that if two events basically occur together, they are correlated. The argument is that correlation attempts to establish a causal relationship. Thus, the claim is that if there was past correlation between two factors, this relationship will also occur in future. For this relationship to occur, there must independent variable influencing the dependent variable. It is the variation of the independent variable (event) that influences variation of the dependent variable (event). In correlation, the two variables exhibit either a positive or negative relation to the discussion in the introduction. Correlation evidence is causal. For example, continuous smoking causes cardiac diseases. The ranges of correlation have a confidence level and margin of error and thus, both variables are not perfect. Further, the sampling variations are not affected by the size of the group. Anscombe’s quartet is a great fallacy illustration that correlation metrics can be wrong.
Part II
Science in Action: Crows' Casual Reasoning
The video demonstrates the ability to inferences regardless of the hidden causal mechanism. It shows how the crow reasons out about the possible outcomes of accidental interventions. Different crows demonstrated different reactions when exposed to an observable event set with a hidden causal agent. Human it a stick and out of the point they come to eat. Even though the stick is left to move by itself, the crows check human presence behind from behind. After a series of rounds, they could come to check behind before going for food. It showed how they responded to a hidden causal agent.
