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Which events are correlated but do not necessarily have a causal relationship?
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Events 2 has a causal relationship with Event 1 when?
Event 1 makes Event 2 happen.
Is it true that two dependent events can have the same probability of occurring?
Yes, it is possible for two dependent events to have the same probability of occurring. The probability of an event is dependent on the outcomes of other events, and it is influenced by the relationship between these events. So, it is conceivable for two dependent events to have equal probabilities.
What are the characteristics of a positive correlation?
First, a correlation is an indicator of the linear relationship between two events or manifestations. As such, it does not indicate that A causes B or B causes A, but rather that A and B coexists together. A correlation will vary between -1 and +1. A correlation of 0 will mean that there is no relationship between A and B. The closer the correlation is to the extreme, the stronger the relationship is. It is important to note that the sign only indicates whether the relationship is positive or negative. More specific to this question, a positive correlation will mean that as A increases, so does B. For example, perfectionism has been found to be positively correlated to depression. In other words, as the person presents more severe form of perfectionism, he or she will also show more symptoms of depression. This relationship could be represented in a graph as a diagonal line starting low and gradually moving higher as it moves towards the right.
What Key word do you associate with sequence of events and what rules of probability do you use?
The key word that I use is causality. However, you cannot use probability to determine causality. Even if two events are highly correlated, probability theory cannot tell whether event A is caused by event B, or event B is caused by event A, or that both are caused by some third event that is not even part of the study.
What events are such that the occurrence of one does not change the probability of other events?
Independent events.
What events are correlated but do not necessarily have a causal relationship?
A Teacher drops A box of chalk, and her chalkboard Crack a few minuets later.
What events are correlated but do not necessarily have causal relationship?
You did not list any events.
Events 2 has a causal relationship with Event 1 when?
Event 1 makes Event 2 happen.
Which events have a causal relationship?
Sam had not eaten breakfast; he was hungry.
How is a wound diagnosed?
A diagnosis is made by visual examination and may be confirmed by a report of the causal events
what is the sequence of casual events in a story?
In a story, causal events typically follow a logical progression where each event is directly influenced by the preceding one. This sequence helps to drive the plot forward and create a coherent narrative. The causal events in a story establish cause-and-effect relationships that lead to the development of characters and the resolution of conflicts.
Who said the animal behaves like a scientist detecting causal relations among events and using a range of information about those events to make relevant inferences?
Rescorla
What is an example of a positive correlation that has an obvious causal relationship?
The standard answer is that a positive statistical correlation, no matter how strong, never proves anything about the causal relationship. Technically, correlations are symmetric and so the evidence is identical whether you imagine that A causes B or B causes A. Another problem is that you could have an omitted third factor C which explains both A and B. A correlation between A and B never rules out the possibility of C influencing them both. What you can sometimes say more realistically is that a strong correlation might make a proposed causal explanation more plausible. It might be evidence as part of an argument, but it's not sufficient by itself. Other parts of the argument could be exclusion of other factors (through experiments or statistical controls) and logical precedence. For example, if you had evidence that women are smarter than men, it doesn't seem likely that smartness causes gender. Similarly, events from the future don't influence events of the past; thus establishing the time sequence might also help to build a causal explanation. In short, there are few if any obvious causal relationships based on correlation alone if you want to use rigorous methods. Experiments and replication of results under diverse circumstances are the best way to show a causal relationship.
What does causalities mean?
A causal inference may not be supported by known facts, but can often be correctly assumed.Right after I saw lightning outside, our electricity went out. (causal: lightning caused the outage)While it was raining very hard, I noticed the window was leaking water. (causal; rainwater found a break around the window)After mom's car hit the pothole, the tire blew. (causal: the sharp edge of the pothole caused the tire to blow)
Correlations are an indication that causality is always present?
False. One of the most important rules to learn in statistics is that correlation does not equal causation. Just because two items or correlated, or linked, doesn't necessarily mean that one caused the other. For example, think about if every time you go out for a run it starts raining. Those two events may be correlated, but that doesn't mean you cause it start raining because you went for a run.
What is a cause-effect inference?
A cause-effect inference is a conclusion or assumption made about the relationship between two events or phenomena, where one event is believed to have caused or influenced the occurrence of the other. It is based on evidence and reasoning that suggests a causal relationship between the two variables.
Is it true if two events are indenpent then the probability of both events is less than 1?
Not necessarily.
