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Why can a scientific theory only be disproven rather …
: The word isn't used much in everyday language, but when it is, it is often applied to ideas that have been shown to be untrue. When that's the case when an idea has been shown to be false a scientist would say that it has been falsified. A falsifi idea, on the other hand, is one for which there is a conceivable that might produce evidence proving the idea false. Scientists and others influenced by the ideas of the philosopher Karl Popper sometimes assert that only falsifiable ideas are scientific. However, we now recognize that science cannot once-and-for-all prove any idea to be false (or true for that matter). Furthermore, it's clear that evidence can play a role in supporting particular ideas over others not just in ruling some ideas out, as implied by the falsifiability criterion. When a scientist says , he or she probably actually means something like , the term we use in this website to avoid confusion. A testable idea is one about which we could gather evidence to help determine whether or not the idea is accurate.
CORRECTION: This misconception may be reinforced by introductory science courses that treat hypotheses as "things we're not sure about yet" and that only explore established and accepted theories. In fact, hypotheses, theories, and laws are rather like apples, oranges, and kumquats: one cannot grow into another, no matter how much fertilizer and water are offered. Hypotheses, theories, and laws are all scientific explanations that differ in breadth not in level of support. Hypotheses are explanations that are limited in scope, applying to fairly narrow range of phenomena. The term is sometimes used to refer to an idea about how observable phenomena are related but the term is also used in other ways within science. Theories are deep explanations that apply to a broad range of phenomena and that may integrate many hypotheses and laws. To learn more about this, visit our page on .
We can’t prove a hypothesis but we can disprove it
CORRECTION: Perhaps because the Scientific Method and popular portrayals of science emphasize , many people think that science can't be done an experiment. In fact, there are ways to test almost any scientific idea; experimentation is only one approach. Some ideas are best tested by setting up a in a lab, some by making detailed observations of the natural world, and some with a combination of strategies. To study detailed examples of how scientific ideas can be tested fairly, with and without experiments, check out our side trip .
You won’t be required to actually perform a real experiment or survey in elementary statistics (or even disprove a fact like “Pluto is a planet”!), so you’ll be given word problems from real-life situations. You’ll need to figure out what your hypothesis is from the problem. This can be a little trickier than just figuring out what the accepted fact is. With word problems, you are looking to find a fact that is nullifiable (i.e. something you can reject).
as they can not be proved; only supported.
The simplest example is solipsism, the philosophical notion that the only thing that really exists is you and that everything you perceive and experience is a figment of your own imagination.
In other words, we simply take out the word "positive", which implies the direction of our effect. In our example, making a two-tailed prediction may seem strange. After all, it would be logical to expect that "extra" tuition (going to seminar classes as well as lectures) would either have a positive effect on students' performance or no effect at all, but certainly not a negative effect. However, this is just our opinion (and hope) and certainly does not mean that we will get the effect we expect. Generally speaking, making a one-tail prediction (i.e., and testing for it this way) is frowned upon as it usually reflects the hope of a researcher rather than any certainty that it will happen. Notable exceptions to this rule are when there is only one possible way in which a change could occur. This can happen, for example, when biological activity/presence in measured. That is, a protein might be "dormant" and the stimulus you are using can only possibly "wake it up" (i.e., it cannot possibly reduce the activity of a "dormant" protein). In addition, for some statistical tests, one-tailed tests are not possible.
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though facts are important, we can only be ..
Fact: are statements that we know to be true through direct . In everyday usage, facts are a highly valued form of knowledge because we can be so confident in them. Scientific thinking, however, recognizes that, though facts are important, we can only be completely confident about relatively simple statements. For example, it may be a fact that there are three trees in your backyard. However, our knowledge of how all trees are related to one another is not a fact; it is a complex body of knowledge based on many different and reasoning that may change as new is discovered and as old evidence is interpreted in new ways. Though our knowledge of tree relationships is not a fact, it is broadly applicable, useful in many situations, and synthesizes many individual facts into a broader framework. values facts but recognizes that many forms of knowledge are more powerful than simple facts.
You can't prove any hypothesis, you can only improve or disprove it.
CORRECTION: Because of some vocal individuals (both inside and outside of science) stridently declaring their beliefs, it's easy to get the impression that science and religion are at war. In fact, people of many different faiths and levels of scientific expertise see no contradiction at all between science and religion. Because science deals only with phenomena and explanations, it cannot support or contradict the existence of entities like God. To learn more, visit our side trip .
You can only fail to disprove it
: In everyday language, an error is simply a mistake, but in science, error has a precise statistical meaning. An error is the difference between a measurement and the true value, often resulting from taking a . For example, imagine that you want to know if corn plants produce more massive ears when grown with a new fertilizer, and so you weigh ears of corn from those plants. You take the mass of your sample of 50 ears of corn and calculate an average. That average is a good estimate of what you are really interested in: the average mass of ears of corn that could be grown with this fertilizer. Your estimate is not a mistake but it does have an error (in the statistical sense of the word) since your estimate is not the true value. Sampling error of the sort described above is inherent whenever a smaller sample is taken to represent a larger entity. Another sort of error results from systematic biases in measurement (e.g., if your scale were calibrated improperly, all of your measurements would be off). Systematic error biases measurements in a particular direction and can be more difficult to quantify than sampling error.
You can only disprove a hypothesis or indicate that it …
If our statistical analysis shows that the significance level is below the cut-off value we have set (e.g., either 0.05 or 0.01), we reject the null hypothesis and accept the alternative hypothesis. Alternatively, if the significance level is above the cut-off value, we fail to reject the null hypothesis and cannot accept the alternative hypothesis. You should note that you cannot accept the null hypothesis, but only find evidence against it.
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