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A good alternative hypothesis is specific to the conditions of the experiment, and its acceptance upon rejecting the null hypothesis offers a possible explanation of what was observed based on evidence obtained by a verifiable process.

The null hypothesis in Redi's experiment, that adult flies were not required to produce maggots, was rejected and eliminated the notion that the maggots arose out of the meat itself - or generated by decaying vegetation, or manure, or swamps, etc.

So, you might get a *p*-value such as 0.03 (i.e., *p* = .03). This means that there is a 3% chance of finding a difference as large as (or larger than) the one in your study given that the null hypothesis is true. However, you want to know whether this is "statistically significant". Typically, if there was a 5% or less chance (5 times in 100 or less) that the difference in the mean exam performance between the two teaching methods (or whatever statistic you are using) is as different as observed given the null hypothesis is true, you would reject the null hypothesis and accept the alternative hypothesis. Alternately, if the chance was greater than 5% (5 times in 100 or more), you would fail to reject the null hypothesis and would not accept the alternative hypothesis. As such, in this example where *p* = .03, we would reject the null hypothesis and accept the alternative hypothesis. We reject it because at a significance level of 0.03 (i.e., less than a 5% chance), the result we obtained could happen too frequently for us to be confident that it was the two teaching methods that had an effect on exam performance.

When considering whether we reject the null hypothesis and accept the alternative hypothesis, we need to consider the direction of the alternative hypothesis statement. For example, the alternative hypothesis that was stated earlier is:

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.

If the *P*-value is less than (or equal to) α, then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the *P*-value is greater than α, then the null hypothesis is not rejected.

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A far-fetched alternative hypothesis that would divert our attention from the central question, would not provide assistance in our understanding of life's processes, and for which we could not collect evidence to reject it, would be that in the middle of the night aliens from another planet placed the maggots in the meat (or that a fairy godmother waved a wand).

Since organic molecules did result, and continue to result whenever the experiment is repeated, we reject the null hypothesis and conclude that the laws of nature are universally valid and we can predict the results of their action if we know those laws.

We invite you to join us on 24-27 July, 2018, for a memorable time in HYPOTHESIS XIII at Singapore.

February 28^{th}, 2018: Abstract Acceptance NotificationMay 31^{st}, 2018: Deadline for Early Bird Registration

If the drought plants wilt and die but the watered plants flower and set seed, then the null hypothesis is rejected in favor of the reasonable alternative.

**CORRECTION: **Journalists often write about "scientific proof" and some scientists talk about it, but in fact, the concept of proof real, absolute proof is not particularly scientific. Science is based on the principle that idea, no matter how widely accepted today, could be overturned tomorrow if the evidence warranted it. Science accepts or rejects ideas based on the evidence; it does not prove or disprove them. To learn more about this, visit our page describing .

AB - The quality of a product might be determined by several parameters, each of which must meet certain standards before the product is acceptable. In this article, a method of determining whether all the parameters meet their respective standards is proposed. The method consists of testing each parameter individually and deciding that the product is acceptable only if each parameter passes its test. This simple method has some optimal properties including attaining exactly a prespecified consumer's risk and uniformly minimizing the producer's risk. These results are obtained from more general hypothesis-testing results concerning null hypotheses consisting of the unions of sets.

**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.

N2 - The quality of a product might be determined by several parameters, each of which must meet certain standards before the product is acceptable. In this article, a method of determining whether all the parameters meet their respective standards is proposed. The method consists of testing each parameter individually and deciding that the product is acceptable only if each parameter passes its test. This simple method has some optimal properties including attaining exactly a prespecified consumer's risk and uniformly minimizing the producer's risk. These results are obtained from more general hypothesis-testing results concerning null hypotheses consisting of the unions of sets.

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