Postglacial range expansion drives the rapid diversification of the genus Pleistocene glacial cycles are thought to have played a major role in the diversification of temperate and boreal species of North American birds. Given that coalescence times between sister taxa typically range from 0.1 to 2.0 Myr, it has been assumed that diversification occurred as populations were isolated in refugia over long periods of time, probably spanning one to several full glacial cycles. In contrast, the rapid postglacial range expansions and recolonization of northern latitudes following glacial maxima have received less attention as potential promoters of speciation. reported a case of extremely rapid diversification in the songbird genus as a result of a single continent-wide range expansion within the last 10000 years. Molecular data from 264 juncos sampled throughout their range reveal that as the Yellow-eyed Junco () of Mesoamerica expanded northward following the last glacial maximum, it speciated into the Dark-eyed Junco (), which subsequently diversified itself into at least five markedly distinct and geographically structured morphotypes in the USA and Canada. Patterns of low genetic structure and diversity in mitochondrial DNA and amplified fragment length polymorphism loci found in Dark-eyed Juncos relative to Mesoamerican Yellow-eyed Juncos provide support for the hypothesis of an expansion from the south, followed by rapid diversification in the north. These results underscore the role of postglacial expansions in promoting diversification and speciation through a mechanism that represents an alternative to traditional modes of Pleistocene speciation. Recent divergence with fastplumage evolution (since the LGM) has been recently documented in orioles, redpolls, bluethroats, yellow wagtails, and Yellow-rumped Warblers. However, given the higher number and marked distinctiveness of plumage morphs and the short amount of time involved, the Dark-eyed Junco complex represents an exceptional case of fast plumage diversification. Given the marked differences in plumage characters, sexual selection may have acted as the mechanism of divergence
-- Causes for the higher biodiversity in the Neotropics as compared to the Nearctic and the factors promoting species diversification in each region have been much debated. The refuge hypothesis posits that high tropical diversity reflects high speciation rates during the Pleistocene, but this conclusion has been challenged. investigated this matter by examining continental patterns of avian diversification through the analysis of large-scale DNA barcode libraries. Datasets from the avifaunas of Argentina, the Nearctic, and the Palearctic were analyzed. Average genetic distances between closest congeners and sister species were higher in Argentina than in North America reflecting a much higher percentage of recently diverged species in North America (Nearctic). In the Palearctic, genetic distances between closely related species appeared to be more similar to those of the southern Neotropics. Average intraspecific variation was similar in Argentina and North America, while the Palearctic fauna had a higher value due to a higher percentage of variable species. Geographic patterning of intraspecific structure was more complex in the southern Neotropics than in the Nearctic, while the Palearctic showed an intermediate level of complexity. This analysis suggests that avian species are older in Argentina than in the Nearctic, supporting the idea that the greater diversity of the Neotropical avifauna is not caused by higher recent speciation rates. Species in the Palearctic also appear to be older than those in the Nearctic. These results, combined with the patterns of geographic structuring found in each region, suggest a major impact of Pleistocene glaciations in the Nearctic, a lesser effect in the Palearctic and a mild effect in the southern Neotropics.
In this setting, the p-value is based on the hull hypothesis and has nothing to do with an alternative hypothesis and therefore with the rejection region.
The test for homogeneity, on the other hand, is designed to test the null hypothesis that two or more , according to some criterion of classification applied to the samples.
When a hypothesis does not predict the expected direction of the results it is referred to as a two-tailed hypothesis. For example a two tailed hypothesis might be that there will be a difference in performance on a memory test between participants who are tested at 10am and participants who are tested at 10pm
When carrying out experiments it is expected that the researcher will start with a hypothesis.A hypothesis is a testable, predictive statement. The hypothesis will state what the researcher expects to find out. For example, participants who are tested at 10am will perform significantly better on a memory test than participants who are tested at 10pm.It is important that the independent and dependent variables are clearly stated in the hypothesis.When a hypothesis predicts the expected direction of the results it is referred to as a one-tailed hypothesis. For example the hypothesis above is stating that participants will perform better in the morning than the evening and is therefore a one-tailed hypothesis.When a hypothesis does not predict the expected direction of the results it is referred to as a two-tailed hypothesis. For example a two tailed hypothesis might be that there will be a difference in performance on a memory test between participants who are tested at 10am and participants who are tested at 10pmThe hypothesis that states the expected results is called the alternate hypothesis because it is alternative to the null hypothesis. When conducting an experiment it is important that we have an alternate hypothesis and a null hypothesis. The null hypothesis is not the opposite of the alternate hypothesis it is a statement of no difference. A null hypothesis might be that there will be no significant difference on the performance on a memory test between participants who are tested at 10am and participants whom are tested at 10pm.The reason we have a null hypothesis is that the statistical tests that we use are designed to test the null hypothesis. Descriptive Statistics
A laboratory experiment is an experiment conducted under highly controlled conditions. The variable which is being manipulated by the researcher is called the independent variable and the dependent variable is the change in behaviour measured by the researcher. All other variables which might affect the results and therefore give us a false set of results are called confounding variables (also referred to as random variables). By changing one variable (the independent variable) while measuring another (the dependent variable) while we control all others, as far as possible, then the experimental method allows us to draw conclusions with far more certainty than any non-experimental method. If the independent variable is the only thing that is changed then it must be responsible for any change in the dependent variable.Laboratory experiments allow for precise control of variables. The purpose of control is to enable the experimenter to isolate the one key variable which has been selected (the independent variable), in order to observe its effect on some other variable (the dependent variable); control is intended to allow us to conclude that it is the independent variable, and nothing else, which is influencing the dependent variable. However, it must also be noted that it is not always be possible to completely control all variables. There may be other variables at work which the experimenter is unaware of.It is argued that laboratory experiments allow us to make statements about cause and effect, because unlike non-experimental methods they involve the deliberate manipulation of one variable, while trying to keep all other variables constant. Sometimes the independent variable is thought of as the cause and the dependent variable as the effect. Furthermore, experiments can usually be easily replicated. The experimental method consists of standardised procedures and measures which allow it to be easily repeated. However laboratory experiments are not always typical of real life situations. These types of experiments are often conducted in strange and contrived environments and the participants mat be asked to carry out unusual tasks. The behaviour of the participants may be distorted and not be like behaviour that would be carried out in the real world. Therefore, it should be difficult to generalise findings from experiments because they are not usually ecologically valid (true to real life). A further difficulty with the experimental method is demand characteristics. Demand characteristics are all the cues which convey to the participant the purpose of the experiment. If a participant knows they are in an experiment they may seek cues about how they think they are expected to behave.Another problem with the experimental method concerns ethics. For example, experiments often involve deceiving participants to some extent. However, it is possible to obtain a level of informed consent from participants. That is, the experimenter can provide information about what is going to happen without giving away the full aim of the study. This helps participants decide if they really want to take part.It is recommended that participants in experiments are effectively debriefed and that the participants are clear that they can withdraw from the study at any time. It is important to recognise that there are very many areas of human life which cannot be studied using the experimental method because it would be simply too unethical to do so. Field experimentsA field experiment is an experiment that is conducted in ?the field ?. That is, in a real world situation. In field experiments the participants are not usually aware that that they are participating in an experiment.The independent variable is still manipulated unlike in natural experiments. Field experiments are usually high in ecological validity and may avoid demand characteristics as the participants are unaware of the experiment. However, in field experiments it is much harder to control confounding variables and they are usually time consuming and expensive to conduct. In field experiments it is not usually possible to gain informed consent from the participants and it is difficult to debrief the participants. Quasi or natural experimentsQuasi experiments are so called because they are not classed as true experiments.A quasi experiment is where the independent variable is not manipulated by the researcher but occurs naturally. These experiments are often called natural experiments. In a true experiment participants are allocated to the conditions of an experiment, usually through random assignment, however this is not always possible for practical or ethical reasons.In a quasi experiment the researcher takes advantage of pre-existing conditions such as age, sex or an event that the researcher has no control over such as a participants? occupation.A strength of some quasi experiments is that participants are often unaware that they are taking part in an investigation and they may not be as artificial as laboratory experiments.However, it is argued that with quasi experiments it is harder to establish causal relationships because the independent variable is not being directly manipulated by the researcher.It is worth noting that quasi experiments are very common in psychology because ethically and practically they are the only design that can be used. Experimental Design An important procedure to be aware of when researchers carry out experiments is experimental design. An independent measures design consists of using different participants for each condition of the experiment. If two groups in an experiment consist of different individuals then this is an independent measures design. This type of design has an advantage resulting from the different participants used in each condition - there is no problem with order effects The most serious disadvantage of independent measures designs is the potential for error resulting from individual differences between the groups of participants taking part in the different conditions. Also an independent groups design may represent an uneconomic use of those participants, since twice as many participants are needed to obtain the same amount of data as would be required in a two-condition repeated measures design. A repeated measures design consists of testing the same individuals on two or more conditions. The key advantage of the repeated measures design is that individual differences between participants are removed as a potential confounding variable. Also the repeated measures design requires fewer participants, since data for all conditions derive from the same group of participants. The design also has its disadvantages. The range of potential uses is smaller than for the independent groups design. For example, it is not always possible to test the same participants twice. There is also a potential disadvantage resulting from order effects, although these order effects can be minimised. Order effects occur when people behave differently because of the order in which the conditions are performed. For example, the participant?s performance may be enhanced because of a practice effect, or performance may be reduced because of a boredom or fatigue effect. Order effects act as a confounding variable but can be reduced by using counterbalancing. If there are two conditions in an experiment the first participant can do the first condition first and the second condition second. The second participant can do the second condition first and the first condition second and so on. Therefore any order effects should be randomised. A matched pairs design consists of using different participants for each condition of the experiment but participant variables are controlled by matching pairs of variables on a key variable. In order to get the pairing precise enough, it is common to get one group of participants together and then look round for partners for everyone. Participants can be matched on variables which are considered to be relevant to the experiment in question. For example, pairs of participants might be matched for their scores from intelligence or personality tests. Although this design combines the key benefits of both an independent and repeated measures design, achieving matched pairs of participants is a difficult and time consuming task which may be too costly to undertake. Successful use of a matched pairs design is heavily dependent on the use of reliable and valid procedures for pre-testing participants to obtain matched pairs.
Patient 1 is a case and he is exposed so he fits into either cell A or cell C. Based upon his control's status we determine which cell is the correct placement for this pair. Patient 1's control is exposed, therefore Patient 1 and Person 47 fit into cell A as a pair. This is a concordant pair because both are exposed. Concordancy is based upon exposure status. In a matched case-control study, the cell counts represent pairs, not individuals. In the statistical analysis, only the discordant pairs are important. Cells B and C contribute to the odds ratio in a matched design. Cells A and D do not contribute to to the odds-ratio. If the risk for disease is increased due to exposure, C will be greater than B.