Establishing Cause and Effect - Scientific Causality
And although such relationships are, in the strict sense, merely very good human beings routinely, and necessarily, use them to assign cause and effect. is in practice not uncommon in scientific research as well. A central goal of most research is the identification of causal relationships, or demonstrating that a particular independent variable (the cause) has an effect on . Cause and effect is one of the most commonly misunderstood concepts in science and is must contain measures to establish the cause and effect relationship.
Here we see that one cause having the status of an all-star athlete has two effects increased self-confidence and higher attractiveness ratings among other students. Cause-Effect Criteria In order to establish a cause-effect relationship, three criteria must be met.
The first criterion is that the cause has to occur before the effect.
This is also known as temporal precedence. In the example above, the students had to become all-star athletes before their attractiveness ratings and self-confidence improved.
For example, let's say that you were conducting an experiment to see if making a loud noise would cause newborns to cry. In this example, the loud noise would have to occur before the newborns cried. In both examples, the causes occurred before the effects, so the first criterion was met. Second, whenever the cause happens, the effect must also occur. Consequently, if the cause does not happen, then the effect must not take place.
The strength of the cause also determines the strength of the effect.
- Cause and Effect Relationship: Definition & Examples
- Australian Bureau of Statistics
- Establishing Cause and Effect
Think about the example with the all-star athlete. The research study found that popularity and self-confidence did not increase for the students who did not become all-star athletes.
Let's assume we also found that the better the student's rankings in sports; that is, the stronger they became in athletics compared to their peers, the more popular and confident the student became. For this example, criterion two is met. Let's say that for our newborn experiment we found that as soon as the loud noise occurred, the newborn cried and that the newborns did not cry in absence of the sound.
A third thing to notice is that physical laws cannot be proved; instead, they can fail to be refuted. The standard methodology of science calls for making hypotheses, and then trying to refute them with experiments. No single experiment can show that a physical law is true, because the law is supposed to apply to an infinite number of different situations.
However, a single experiment can refute a hypothesis. A hypothesis that withstands more and more tests gradually comes to be more and more accepted. And as already noted, even experiments that contradict a law may not serve to refute it totally, but only to restrict the range of phenomena within which it is considered to be usefully valid. If this is the situation for well-established and widely used physical theories, just think how bad things must be for the social sciences!
Statistical Language - Correlation and Causation
It is often said that good science is reductionist, and the success of the hard sciences is often given as an example that the social sciences should try to follow. A prime example is the reduction of chemistry to physics.
However, if we look carefully at what really happens in chemistry, we will see that chemists are not doing a specialized kind of physics. On the contrary, they are using concepts at the level of chemistry, such as valance. Since it is impossible in practice to solve Schroedinger's equation for any but the very simplest atoms, calculations in quantum physics cannot be used to do chemistry.
This situation is often described by saying that chemistry is an emergent level above physics, meaning that partial reductions are possible and can be very valuable when they occur, but concepts and theories that are distinctly chemical and not quantum mechanics are primary for the practical applications. This does not deny that reduction might be possible in principle, and most scientists believe that this is the case.
If we look at higher levels, such as biology, psychology, and sociology, we again see emergent phenomena, but it becomes progressively more difficult to support the belief that reduction to lower levels must be possible in principle, and indeed most social scientists today do not believe this. So where does this leave us?
So we conclude that arguments in favor of technological determinism based on a claim that it is in some sense more scientific than alternatives are fatally flawed.
Going a little further, I think we should conclude that it is very wise to be suspicious of simplistic principles and simplistic arguments in complex areas like the relationship between technology and society. Technological determinism is a prime example of such a simplistic principle. But then, Why, given the deficiencies of technological determinism, do people find it so persuasive? Why is it so common in advertisements, newspaper and magazine articles, websites, and other places?
One answer is that causal explanations are built into our language. For example, the sentence John hit the ball. Readers want to understand this sentence, not in isolation, but as a part of a story, which might be about baseball, where the actor has an intention to perform the action, because of its consequences.
That is, readers want to find a cause, e. Linguistics has developed extensive theories of stories, which can add many interesting details to this discussion. The narrative presupposition applies to any story, but especially to oral narratives of personal experience; it says that the order of clauses is the same as the order of the events that they describe unless there are explicit contrary indications this term was introduced by William Labov.
For example, if we hear John hit the ball. And we will further assume that the crowd cheered because John hit the ball.
Exploring Our Fluid Earth
This is an example of what I call the causal presupposition, which says that if possible, we should read the second event as caused by the first event. Note that the causal presupposition assumes the narrative presupposition. Here is a more complex example: John hit the ball, fell off his bike, and broke his arm. Here there are three narrative clauses, and as before, we automatically assume that the events they describe occur in the same order as the clauses, and furthermore that there are causal relationships between the first and the second event, and between the second and the third event.
Additional evidence concerning the narrative and causal presuppositions comes from studies of the Balinese language, in which the narrative presupposition is replaced by the default presupposition that, given clauses A, B in that order, the corresponding events happen concurrently, possibly with mutual interaction see papers by Alton Becker. In computer science terms, we might say that in English, the default semantic connection between subsequent clauses is ";" rather than " "whereas the opposite holds in Balinese.
Much more information on the theory of narrative can be found on the web through the links on the narratology page at the Media and Communication Studies site at the University of Aberdeen e. More details of my own approach can be found in the essay Notes on Narrative ; I hope that we can discuss narrative further later on in this course.
The basic Volterra-Lotka differential equation is well known, and has as a solution given suitable coefficients and initial values two periodic functions with a time lag; that is, the numbers of wolves and rabbits fluctuate up and down over some fixed time period, as illustrated by the applet below.
Of course, most real ecological systems are much more complex than this, but simple cases where the assumptions of this model are satisfied have actually been observed in nature.