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Events modify impressions on all three EPA dimensions in complex ways that are described with non-linear equations obtained through empirical studies. [5] Here are two examples of impression-formation processes. An actor who behaves disagreeably seems less good, especially if the object of the behavior is innocent and powerless, like a child.
Causality is an influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is at least partly responsible for the effect, and the effect is at least partly dependent on the cause. [1]
In essence probability is influenced by a person's information about the possible occurrence of an event. For example, let the event be 'I have a new phone'; event be 'I have a new watch'; and event be 'I am happy'; and suppose that having either a new phone or a new watch increases the probability of my being happy.
One of the goals of relativity is to specify the possibility of one event influencing another. This is done by means of the metric tensor, which allows for determining the causal structure of spacetime. The difference (or interval) between two events can be classified into spacelike, lightlike and timelike separations. Only if two events are ...
Causality is the relationship between causes and effects. [1] [2] While causality is also a topic studied from the perspectives of philosophy and physics, it is operationalized so that causes of an event must be in the past light cone of the event and ultimately reducible to fundamental interactions.
However, in logic, the technical use of the word "implies" means "is a sufficient condition for." [3] That is the meaning intended by statisticians when they say causation is not certain. Indeed, p implies q has the technical meaning of the material conditional: if p then q symbolized as p → q. That is, "if circumstance p is true, then q ...
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Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes.Two events are independent, statistically independent, or stochastically independent [1] if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, does not affect the odds.