This is considered a much higher % when compared to the ponds with a lower degree of parasitism.
Why did the scientists hypothesize there would be a difference in fitness between the sexual and asexual snails in a pond with different degrees of parasitism?
Are the data they obtained consistent with Weismann's hypothesis?
CORRECTION: This misconception likely stems from introductory science labs, with their emphasis on getting the "right" answer and with congratulations handed out for having the "correct" hypothesis all along. In fact, science gains as much from figuring out which hypotheses are likely to be wrong as it does from figuring out which are supported by the evidence. Scientists may have personal favorite hypotheses, but they strive to consider multiple hypotheses and be unbiased when evaluating them against the evidence. A scientist who finds evidence contradicting a favorite hypothesis may be surprised and probably disappointed, but can rest easy knowing that he or she has made a valuable contribution to science.
I tend to agree with you. However, a primary problem with proposal writing is that reviewers and study sections do NOT seem to agree on the relative importance of hypothesis-driven, question-driven and unbiased exploratory research. I may write a proposal involving question-based language or a “fishing expedition” that seems necessary, but some reviewers might want to see me test an “overarching hypothesis”. How in the world are applicants supposed to anticipate how study sections will receive their approaches unless NIH comes up with a set of guiding principles that reviewers are always reminded of?
They can’t all be unlucky in theirsamples, can they?If you keep giving the universe opportunities to send you datathat contradict the null hypothesis, but you keep getting data that areconsistent with the null, then you begin to think that thenull hypothesis shouldn’t be rejected, that it’sactually true.This is why scientists always replicate experiments.
: 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.
Hypothesis testing is very important in the scientific community and is necessary for advancing theories and ideas. Statistical hypothesis tests are not just designed to select the more likely of two hypotheses—a test will remain with the null hypothesis until there's enough evidence to support the alternative hypothesis. Now you have seen several examples of hypothesis testing and you can better understand why it is so important. For more information on types of hypotheses see .