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If evidence supports a hypothesis, it is upgraded to a theory.

They interpret the fossil evidence as supporting two separate hypotheses:

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Theories apply to a broader range of phenomena than do hypotheses.

To understand the distinction between hypothesis and theorem often allows non-scientists to assess the validity of most discussions about science without ever needing to see the raw data or understand deeply the scientific analysis of it. A huge proportion of the articles written about hot scientific topics, such as anthropomorphic global warming, make arguments (pro- and con-) that claim to be based in scientific fact, but are in fact presenting hypothesis and not theorem. Often they do not use scientific arguments at all. They might for example give anecdotal stories based upon personal memories about how hot or cold it was in the past compared to now, especially if we are now going through a cold snap or a heat wave. Notwithstanding the unreliability of memory in gauging temperature, this ignores the fact that extreme conditions do not indicate general trends. Sometimes we are told that something must be true because ‘2,000 scientists say so’ (when democracy is not a principle for determining truth in science – it has to be tested using objective standards); or tell us we have to act now because the penalties of not doing so are too great to contemplate (without regard to the actual likelihood of it being true, which is absurd). None of these types of argument should be taken seriously when considering whether or not something is true and demanding greater clarity from our journalists might actually force them to be more responsible and rigorous in reporting on such things (although I’m not holding my breath).

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 .

Of course later on this hypothesis was disproved.

Hypotheses, theories, and laws are all scientific explanations but they differ in breadth, not in level of support.

Its central ideas are supported by the weight of scientific evidence available (including the fossils discovered by the Leakey team), have been investigated by scientists the world over for more than a century, and continue to provide useful insights into fields as diverse as conservation biology, agriculture, computer programming, and human health.

CORRECTION: This misconception is based on the idea of falsification, philosopher Karl Popper's influential account of scientific justification, which suggests that all science can do is reject, or falsify, hypotheses — that science cannot find evidence that one idea over others. Falsification was a popular philosophical doctrine — especially with scientists — but it was soon recognized that falsification wasn't a very complete or accurate picture of how scientific knowledge is built. In science, ideas can never be completely proved or completely disproved. Instead, science accepts or rejects ideas based on supporting and refuting evidence, and may revise those conclusions if warranted by new evidence or perspectives.

support his continental drift hypothesis

For example, the watchmaker analogy which was proposed back in the 17th century as a means of explaining the creation of the universe by God.

The film covers the story of Elaine Morgan and her dogged and successful quest to pierce the official scientific orthodoxy, by promoting a hypothesis for an aquatic phase in the development of the human phenotype. The idea that an important phase of human evolution took place in the water helps to explain our relative hairlessness in comparison to other primates, as well as the layer of fat beneath our skin, our being the only bipedal mammals and our ability to control our breathing, which is a prerequisite for the power of speech, along with many other distinctively human traits.

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.

For example, the spacecraft
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  • to a hypothesis “supported by a vast ..

    It's able to conduct a series of experiments in the lab which include observations, hypothesis, and conclusion.

  • The Hypothesis of Evolution And Creation Science

    Wegener used fossil evidence to support his continental drift hypothesis

  • The Hypothesis of Evolution And Creation Science

    with the trans-Mexico dispersal hypothesis supported by the new discovery

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between observations and the hypothesis supported by them) ..

: In everyday language, the word usually refers to an educated guess — or an idea that we are quite uncertain about. Scientific hypotheses, however, are much more informed than any guess and are usually based on prior experience, scientific background knowledge, preliminary observations, and logic. In addition, hypotheses are often supported by many different lines of evidence — in which case, scientists are more confident in them than they would be in any mere "guess." To further complicate matters, science textbooks frequently misuse the term in a slightly different way. They may ask students to make a about the outcome of an experiment (e.g., table salt will dissolve in water more quickly than rock salt will). This is simply a prediction or a guess (even if a well-informed one) about the outcome of an experiment. Scientific hypotheses, on the other hand, have explanatory power — they are explanations for phenomena. The idea that table salt dissolves faster than rock salt is not very hypothesis-like because it is not very explanatory. A more scientific (i.e., more explanatory) hypothesis might be "The amount of surface area a substance has affects how quickly it can dissolve. More surface area means a faster rate of dissolution." This hypothesis has some explanatory power — it gives us an idea of a particular phenomenon occurs — and it is testable because it generates expectations about what we should observe in different situations. If the hypothesis is accurate, then we'd expect that, for example, sugar processed to a powder should dissolve more quickly than granular sugar. Students could examine rates of dissolution of many different substances in powdered, granular, and pellet form to further test the idea. The statement "Table salt will dissolve in water more quickly than rock salt" is not a hypothesis, but an expectation generated by a hypothesis. Textbooks and science labs can lead to confusions about the difference between a hypothesis and an expectation regarding the outcome of a scientific test. To learn more about scientific hypotheses, visit in our section on how science works.

evidence has not yet supported the hypothesis.

: In everyday language, a is a rule that must be abided or something that can be relied upon to occur in a particular situation. Scientific laws, on the other hand, are less rigid. They may have exceptions, and, like other scientific knowledge, may be modified or rejected based on new evidence and perspectives. In science, the term usually refers to a generalization about and is a compact way of describing what we'd expect to happen in a particular situation. Some laws are non-mechanistic statements about the relationship among observable phenomena. For example, the ideal gas law describes how the pressure, volume, and temperature of a particular amount of gas are related to one another. It does not describe how gases behave; we know that gases do not precisely conform to the ideal gas law. Other laws deal with phenomena that are not directly observable. For example, the second law of thermodynamics deals with entropy, which is not directly observable in the same way that volume and pressure are. Still other laws offer more mechanistic explanations of phenomena. For example, Mendel's first law offers a of how genes are distributed to gametes and offspring that helps us make about the outcomes of genetic crosses. The term may be used to describe many different forms of scientific knowledge, and whether or not a particular idea is called a law has much to do with its discipline and the time period in which it was first developed.

This hypothesis of the "missing inferior evolutionary ..

: 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.

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