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used to test the egg­viability hypothesis in ..

What predictions can researchers make based on the egg viability hypothesis? - 5116400

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A test of the egg-viability hypothesis.

A scientific theory starts out as a hypothesis, a proposed explanation for some phenomenon, and where possible, the hypothesis is subjected to testing. Scientists document such work and publish their papers in peer-reviewed science journals. Other scientists in the same discipline review and comment on the paper before it sees the light of day, and other researchers should be able to replicate the experimental results.

of Ornithology and Greg Butcher at Audubon tested the egg viability hypothesis, ..

One aspect of the -test that tends to agitate users is the obligation to choose either the one or two-tailed versions of the test. That the term “tails” is not particularly informative only exacerbates the matter. The key difference between the one- and two-tailed versions comes down to the formal statistical question being posed. Namely, the difference lies in the wording of the research question. To illustrate this point, we will start by applying a two-tailed -test to our example of embryonic GFP expression. In this situation, our typical goal as scientists would be to detect a difference between the two means. This aspiration can be more formally stated in the form of a or . Namely, that the average expression levels of ::GFP in wild type and in mutant are different. The must convey the opposite sentiment. For the two-tailed -test, the null hypothesis is simply that the expression of ::GFP in wild type and mutant backgrounds is the same. Alternatively, one could state that the difference in expression levels between wild type and mutant is zero.

Factors affecting egg viability and ..

Researchers have rarely considered how air temperature affects the survival of eggs before they are incubated (the Egg Viability Hypothesis).

proportions or distributions refer to data sets where outcomes are divided into three or more discrete categories. A common textbook example involves the analysis of genetic crosses where either genotypic or phenotypic results are compared to what would be expected based on Mendel's laws. The standard prescribed statistical procedure in these situations is the test, an approximation method that is analogous to the normal approximation test for binomials. The basic requirements for multinomial tests are similar to those described for binomial tests. Namely, the data must be acquired through random sampling and the outcome of any given trial must be independent of the outcome of other trials. In addition, a minimum of five outcomes is required for each category for the Chi-square goodness-of-fit test to be valid. To run the Chi-square goodness-of-fit test, one can use standard software programs or websites. These will require that you enter the number of expected or control outcomes for each category along with the number of experimental outcomes in each category. This procedure tests the null hypothesis that the experimental data were derived from the same population as the control or theoretical population and that any differences in the proportion of data within individual categories are due to chance sampling.

Once you understand the idea behind the two-tailed -test, the one-tailed type is fairly straightforward. For the one-tailed -test, however, there will always be two distinct versions, each with a different research hypothesis and corresponding null hypothesis. For example, if there is sufficient reason to believe that GFPwt will be greater than GFPmut , then the research hypothesis could be written as

Incubation time was related to egg size in ..

Egg-Viability Hypothesis

Most statistical tests culminate in a statement regarding the -value, without which reviewers or readers may feel shortchanged. The -value is commonly defined as the probability of obtaining a result (more formally a ) that is at least as extreme as the one observed, assuming that the is true. Here, the specific null hypothesis will depend on the nature of the experiment. In general, the null hypothesis is the statistical equivalent of the “innocent until proven guilty” convention of the judicial system. For example, we may be testing a mutant that we suspect changes the ratio of male-to-hermaphrodite cross-progeny following mating. In this case, the null hypothesis is that the mutant does not differ from wild type, where the sex ratio is established to be 1:1. More directly, the null hypothesis is that the sex ratio in mutants is 1:1. Furthermore, the complement of the null hypothesis, known as the or , would be that the sex ratio in mutants is different than that in wild type or is something other than 1:1. For this experiment, showing that the ratio in mutants is different than 1:1 would constitute a finding of interest. Here, use of the term “significantly” is short-hand for a particular technical meaning, namely that the result is , which in turn implies only that the observed difference appears to be real and is not due only to random chance in the sample(s). . Moreover, the term significant is not an ideal one, but because of long-standing convention, we are stuck with it. Statistically or statistically may in fact be better terms.

Egg viability as a constraint on seasonal and latitudinal trends in ..
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  • Japanese Journal of Ornithology 63:2, 197-233

    Central Texas College

  • Waterfowl begin incubating their eggs before the clutch is completed

    Avian clutch size - Wikipedia

  • No current hypotheses can explain this phenomenon

    The hypothesis states that avian clutch size differences arise from differences in food availability

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