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The null hypothesis is typically assumed to be true in statistical hypothesis testing. It represents the scenario where there is no significant difference or effect observed between groups or conditions being compared. Researchers seek evidence to reject the null hypothesis in favor of an alternative hypothesis that suggests a real difference or effect exists.
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On average what value is expected for the t statistic when the null hypothesis is true?
When the null hypothesis is true, the expected value for the t statistic is 0. This is because the t statistic is calculated as the difference between the sample mean and the hypothesized population mean, divided by the standard error, and when the null hypothesis is true, these values should be equal, resulting in a t statistic of 0.
What are universal hypotheses?
Universal hypotheses are general statements or predictions that are widely applicable across different contexts or situations. These hypotheses aim to describe a broad pattern or relationship that is believed to hold true in a variety of scenarios. They are often used in scientific research to make predictions that can be tested and verified.
How is null hypothesis tested?
The null hypothesis is typically tested using statistical tests such as t-tests, ANOVA, or chi-square tests. These tests calculate the probability of obtaining the observed data if the null hypothesis were true. If this probability (p-value) is below a certain threshold (usually 0.05), the null hypothesis is rejected.
Water can change from one state to one state to another true or false?
True. Water can change from a solid state (ice) to a liquid state (water), and from a liquid state to a gaseous state (steam) depending on the temperature and pressure conditions.
Does a hypothesis test ever prove the null hypothesis?
The traditional view that one cannot prove the null hypothesis by a statistical analysis is a consequence of Ronald Fisher's structuring of the problem of probabilistic inference. Fisher argued that if one wanted to determine whether an experimental manipulation (e.g., a drug treatment or a treatment of some crop land) had an effect (on e.g., recovery rate or crop yield), one should compute the probability that one would obtain an effect (that is, a difference in the means of two samples, a control sample, and an experimental sample) as big or bigger than the one in fact obtained if both samples were in fact drawn from the same distribution (that is, if there were in fact no effect). In other words, how likely is it that one would see an effect that big or bigger by chance? If this probability is sufficiently low (say, less than one chance in 20), then one is justified in concluding that what one did had an effect (or that there was a difference in the average values in the populations that one drew the two samples from). Thus, if the difference in the means of the two samples was sufficiently greater than expected by chance, then one was justified in concluding that something more than chance was at work. This way of framing the problem has come to be called Null Hypothesis Significance Testing (NHST, for short). In this formulation, you cannot prove the null hypothesis, because failing to reject it is not the same as accepting it--anymore than a verdict of "not proven" is the same as a verdict of "not guilty." Thus, if you think this is the right way to formulate the problem of probabilistic inference, then you cannot prove the null. But this way of formulating the problem flies in the face of common sense. We all draw a strong and clear distinction between "not proven" and "not guilty." In this formulation, the only possible verdict as regards the null hypothesis is "not proven." This would perhaps be okay if null hypotheses were of no scientific or practical importance. But, in fact, they are of profound scientific and practical importance. The conservation laws, which are at the foundation of modern physics, are null hypotheses; they all assert that "under no circumstance does this change." And, for most people it matters whether a generic drug costing 1/10 the cost of a brand drug really has "the same effect" as the brand drug or just "has not been proven to have a different effect." If you frame it in the latter way, then many more people will opt to pay the additional cost than if you say that "the evidence shows that the effects of the generic drug and the brand drug do not differ" (a null hypothesis). Moreover, and this is more technical, the traditional formulation violates basic mathematical/logical considerations. One of these is consistency: a rational treatment of the evidence for and against any hypothesis should have the property that as the number of observations "goes to infinity" (becomes arbitrarily large), then the probability of drawing the correct conclusion should go to 1. But, under the NHST formulation, when the null hypothesis is true, the probability of rejecting it remains .05 or .01 (whatever one regards at the crucial degree of improbability) no matter how many observations there are. Another curious aspect of the traditional formulation is that it licenses concluding the one's own (non-null) hypothesis is correct because the null hypothesis appears to fail, even though one's own hypothesis is never tested against the data, whereas the null hypothesis is. This is a little bit like concluding that one could oneself climb a formidable mountain just because someone else has failed to climb it. Fairness would seem to require that one's own hypothesis, whatever it may be, should undergo the same test that the null hypothesis has undergone. At a somewhat simpler level, How can a statistical method for drawing conclusions be valid if it prohibits ever drawing a conclusion in favor of some hypotheses (null hypotheses) that are of fundamental scientific and practical importance? There is an alternative to the NHST formulation of the problem of probabilistic inference that dates back to the work of the Reverend Thomas Bayes in the 18th century. In the Bayesian formulation, both the null hypothesis and one or more alternatives to it are tested against the data. In this formulation, each of the hypotheses places a bet on where the data from the experimental treatment (or the other condition of observation) will fall. The hypothesis that does the best job of anticipating where the data in fact fall obtains the greatest odds of being correct (or, at least, more valid than the alternatives to it that have been proposed). In this formulation, it is perfectly possible for the null hypothesis to be the odds on favorite. In other words, in this conception of how to do probabilistic inference, it is possible to prove the null in the sense that the null may have arbitrarily greater odds as against any of the proposed alternative to it. Thus, in this formulation, the null is no different than any other hypothesis. This approach to the problem of probabilistic inference has gained considerable currency in recent years. According to its advocates, it is the only "normative" (mathematically correct) formulation, because, among other things, it does not prohibit any hypothesis from being accepted, and because it is consistent: as the data (number of observations) become arbitrarily large, the odds that the true hypothesis will be accepted increase toward infinity, regardless of whether the true hypothesis is the null hypothesis or an alternative to it. In short, the Bayesian formulation places the null hypothesis on the same footing as any other hypothesis, so it is just as susceptible of proof as any other hypothesis.
The following statements are true about hypotheses and theories except?
Hypotheses and theories are proven to be true.
Does failing to reject null mean null is true?
NO!
If null is compared with null what is the result-true or false or null or unknown?
You mean SQL? NULL = anything IS NULL NULL <> anything IS NULL ... NULL IS NULL = TRUE NULL IS NOT NULL = FALSE
How do you perform a Statistical Hypothesis Testing?
To start with you select your hypothesis and its opposite: the null and alternative hypotheses. You select a confidence level (alpha %), which is the probability that your testing procedure rejects the null hypothesis when, if fact, it is true.Next you select a test statistic and calculate its probability distribution under the two hypotheses. You then find the possible values of the test statistic which, if the null hypothesis were true, would only occur alpha % of the times. This is called the critical region.Carry out the trial and collect data. Calculate the value of the test statistic. If it lies in the critical region then you reject the null hypothesis and go with the alternative hypothesis. If the test statistic does not lie in the critical region then you have no evidence to reject the null hypothesis.
Is it true that federal and state cases start in different courts?
it is very true.
Is it true or false that the national government can enter a state to restore order in cases of national violence?
this is true
P-value if the null hypothesis is true?
0.5
Is it true that a hypotheses about historical singular events are not testable by the scientific method because they are not repeatable?
True
If the null hypothesis is true and the researchers do not reject it then a correct decision has been made?
True because the point of the hypothesis test is to figure out the probability of the null hypothesis being true or false. If it is tested and it is true, then you do not reject but you reject it, when it is false.
If we reject the null hypothesis what can we conclude about the alpha risk?
If we reject the null hypothesis, we conclude that the alternative hypothesis which is the alpha risk is true. The null hypothesis is used in statistics.
A NULL value is treated as a blank or 0 1.true 2.false?
true
Are hypothesis always explicit?
no.because hypotheses you want to test if it is true or not
