What is the definition of a p-value in a hypothesis test?
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try this site\n\nhttp://www.futureaccountant.com/probability/
895648
Null Hypothesis: a statementof equality; a statement of no difference; a statement of chance.  In the case of a hypothesis test involving a single sample mean (that is compared to a known population mean), the null is typically a statement of the value of the population mean.  In otherwords, a hypothesis that predicts the relationship of two variables being equal.  (Eg. Saying that violence in children is equal to the amount of violent tv they watch)\n\nAlternative Hypothesis: a hypothesis that stands in opposition to the null hypothsis.  It may be directional or nondirectional.  (Eg. saying that violent tv watching has NO effect of violence in chilldren.)
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Here's how it works.  You want to test if there is an effect to something, say whether a medicine works.\n\nThe null hypothesis *assumes that the medicine does not work.  (The alternative hypothesis would then be that it does).  When you perform a significance test, you will get a p-value.  Let me explain p-values like this:\n\nYou know a coin comes up heads half the time; however, you would not be surprised if you flipped a coin 10 times and only got 4 heads.  This is obviously due to some slight random chance.\nBut how do we determine what is random chance and what is a real effect?  With a p-value.  The p-value tells us the probability that an event occurred solely due to random chance.\n\nIf you are testing the medicine and you get a p-value of 0.333, that says that, if the medicine isn't working, you'll get those results 1 in 3 times, just by chance.  But if the p-value is 0.001, that says that the results that you got are SO extreme, they would only occur 1 in 1000 times by chance.  Here, it seems likely that the medicine is having a significant effect.\n\nGenerally, 0.05 is considered a rough mark for whether or not some result is due to chance.
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the null hypothesis takes the value of the sample mean while the H1 takes either greater or less the sample mean(1 tailed test)\nu can alaso take the unbiased estimate depending on thequest
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Hypothesis is your best guess as a prediction of the outcome of an experiment.  For example, I have 18 plants, all the same type of plant.  I separate into two groups.  They will get the same water, same sunlight, same air and temperatures for 3 weeks.  The groups will be separated by a sound-proof barrier. \n\nGroup A will have classical music playing, and Group B will have heavy metal playing, both at the same volume.  \n\nNow for the HYPOTHESIS: Group A will grow more and Group B will wither and suffer.  That is my best guess as to the effects of different types of music on plants.\n\nNow we carry out the experiment and see if your RESULTS match your HYPOTHESIS.  That is really the cornerstone of the scientific method.  Conclusions are drawn based on the results and how they correlated with the hypothesis.
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a hypothesis is an educated guess\nit has to be written as an if, then statement\n For example: If this statement helps you, then i mite get 10 points for the best answer!\ngood luck!
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D) - for why?\nbecause\na) is not a hypothesis it is a question\nb) is not talking about the effect of temperature - it is talking about the difference between brands\nc) is only testing brand X and not gelatin in general\n\nD) is a hypothesis - an assumption that we are designing experiments to prove,
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Appartus is not a word, I think you might have it misspelled.  Could it be Apparatus?  If thats it.....\n\nApparatus has 2 definitions - \n1. equipment designed to serve a specific function\n2. anatomy) a group of body parts that work together to perform a given function\n\nHypothesis - An hypothesis is a limited statement regarding cause and effect in specific situations; it also refers to our state of knowledge before experimental work has been performed and perhaps even before new phenomena have been predicted. To take an example from daily life, suppose you discover that your car will not start. You may say, "My car does not start because the battery is low." This is your first hypothesis. You may then check whether the lights were left on, or if the engine makes a particular sound when you turn the ignition key. You might actually check the voltage across the terminals of the battery. If you discover that the battery is not low, you might attempt another hypothesis.\n\nIndependent Variable - the independent variable is the variable which is manipulated or selected by the experimenter to determine its relationship to an observed phenomenon (the dependent variable). In other words, the experiment will attempt to find evidence that the values of the independent variable determine the values of the dependent variable (which is what is being measured). The independent variable can be changed as required, and its values do not represent a problem requiring explanation in an analysis, but are taken simply as given.\n\nDependent Variable -  dependent variable is a factor whose values in different treatment conditions are compared. That is, the experimenter is interested in determining if the value of the dependent variable varies when the values of another variable – the independent variable – are varied, and by how much.\n\nIn simple terms, the independent variable is said to cause an apparent change in, or simply affect, the dependent variable. In analysis, researchers usually want to explain why the dependent variable has a given value. In research, the values of a dependent variable in different settings are usually compared.
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It is how the values (data samples) are laid out over the numbers in your population.  I'm assuming you are referring to the empirical distribution function, which is a step function showing, at each X, for example, how many data points are less than X.\n\nTry this website for more definitions:\nhttp://www.stat.berkeley.edu/~stark/SticiGui/Text/gloss.htm
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As to part one, I can not recommend any acceptable, known test for such data (perhaps what I've written below might work).\n\nAs to part two, this is a horrid approximation since the midrange as the mean is a major assumption (even more far-reaching than assuming something about the populations' probability distributions).\n\nFor part 3 and also part 1:\n\nThe first problem you run into is that you do not know anything about the nature of the population distribution (normal, uniform, etc).  Thus, a nonparametric test would be required.  I know of no nonparametric tests for any of the statistics you have (minimum, maximum or range).\n\nIf it is possible to obtain the actual values for each of the samples in each set, the Kruskal-Wallis test should suffice.  It is nonparametric, so there are no problems with the requirement of assumptions about population distributions.\n\nFurther, its job is to determine if two samples are far enough apart such that the null hypothesis that the population distributions are the same can be rejected.  This would give you what you desire.\n\nAs far as your problem is concerned, the only statistics you have are minimum, maximum and range.   Perhaps one could derive a test to test the minima or maxima of each set, but one still runs into the problem that there can be no parameter placed on the population distribution.\n\nIf you can make an assumption about the distributions of the populations of interest (normal or whatnot), then it would be possible to derive such a test.  This would be a tedious task, however, as it would require thorough knowledge of order statistics and distribution functions.\n\nOne would need to use a test statistic based of off the difference in the two statistics that you use (I would use the maxima since they are further apart than the minima and probability distributions for ranges are uncharted territory unless you're a Ph.D.).  Thus, your test statistic is based off of the difference between the maxima (Y(n) - Z(n) where the (n) are subscripts, which is 2 in this case).\n\nTo perform this test, you would need to derive the probability distribution of the difference of the two maxima and use that to find the probability of the difference being two or greater.  This is quite a task for anyone and requires a large amount of knowledge in mathematical statistics.  I believe I could perform such a task, but it would likely take some time to complete.\n\nRemember though that you must make a critical assumption about the population before you can even do this.  I would recommend using the assumption of uniformity if you are unsure of the populations' distributions.  This is because the derived test would then be least likely to give a type I error, where you reject the data improperly, because the test is less likely to reject.
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A tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation. \nSomething taken to be true for the purpose of argument or investigation; an assumption. \nThe antecedent of a conditional statement. \nok :-0
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a hypothesis is an educated guess on a problem or experiment.
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chi-sq is 5.964  this is like the arithmetic mean\ndf or degrees of freedom (n-1)  is for your t test\np-value is given the null is true that is the probability of obtaining an extreme value from the sample
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since your P-value is large then you cannot reject Ho,
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P(selecting a person at random and he has the symptom and is detected by the procedure)\n\n= P(patient having it) and P(detecting it using the procedure)\n\n= (0.5/100)*(98/100)\n= 0.0049\n\nBut if the person is not randomly selected and you are sure s/he has the problem and its just a matter of detecting it with the procedure, then Probability = (98/100) = 0.98
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The first sample proportion is p1 = 30/100. Its variance is (30/100)*(1-30/100)/100.\n\nThe second sample proportion is p2=60/80. Its variance is (60/80)*(1-60/80)/80.\n\nWhen you look at the difference, p1-p2, and assume that the two proportions are independent then the standard deviation is\nsqrt(var1+var2)=\nsqrt(  (30/100)(1-30/100)/100 + \n          (60/80)(1-60/80)/80)\nwhich is approximately 0.06666146.\n\nHere you can use the normal approximation for both proportions, and the difference is \n30/100-60/80 = -.45.\nIf you use a two sided test then the z-score is -.45/0.06666146 = -6.75, and the p-value is 1.473087e-11. It is highly likely that the difference is zero! Please note that this last part is not quite right. If you want to do a formal hypothesis test then you need to do a pooled estimate.
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The Z-test is a statistical test used in inference which determines if the difference between a sample mean and the population mean is large enough to be statistically significant.\n\nIn order for the Z-test to be reliable, certain conditions must be met. The most important is that since the Z-test uses the population mean and population standard deviation, these must be known. The sample must be a simple random sample of the population. If the sample came from a different sampling method, a different formula must be used. It must also be known that the population varies normally (i.e., the sampling distribution of the probabilities of possible values fits a standard normal curve). If it is not known that the population varies normally, it suffices to have a sufficiently large sample, generally agreed to be ≥ 30 or 40.\n\nIn actuality, knowing the true σ of a population is unrealistic except for cases such as standardized testing in which the entire population is known. In cases where it is impossible to measure every member of a population it is more realistic to use a t-test, which uses the standard error obtained from the sample along with the t-distribution.