Why are null and alternative hypothesis mutually exclusive?
342163
The purpose is to Throw One out ... \n\nExample:  If you wanted to see if better lunches made students happier, you first have to determine "Did it make a difference."  Once you've determined that it has, you can then check to see if it was favorable or not ...\n\nAll of this is random, so you could also say:\n\nHo:  0 difference\nHa1: + difference\nHa2: - difference\n\nBut under the null hypotheis, the presumed TRUE nature of things, if you made a mistake there due to just dumb luck of the draw, you would undoubtly make a mistake in the next ones ...\n\nHypothesis testing is tough to understand ... think of it this way ... the Null Hypothesis is usually that nothing changed.  Once you establish that, say at 95% confidence ... oops, remember, there's a 5% chance that dumb luck just hosed you ...\n\nIf you make any further statements such as + or - changes, based on the 5% change you got hosed by dumb luck, then your true, total chance of error is actually far greater ...\n\nLook up the Bonferroni Correction on Google.\n\nFor the time being, however, just make your life simpler and accept the fact that the mathematics and calculus get real messy real quick.\n\nWhat about this example:  Will you Marry Me (just kidding here).   That's sort of a Yes/No question ... what am I supposed to do with a "Maybe" ... the whole purpose of asking the question in the first place was to get a definiative answer.
6514
False.\n\nThe key word in the question is 'always'.  If there is any probability of a different answer, no matter how small, then it can not be 'always'.
245200
Proof by contradiction: Assume A or B.\n\nIf not B then not A\nalso not C (because of not B) so neither A nor C which contradicts our assumption. Therefore B.
679464
I find it hard to believe that the pairs are treated independently. There's no point to that, it doesn't give you any useful statistical information.\n\nHowever, if you're trying to compare all the values on the left side as a group with their pairs on the right side as a group, then you could use a modified version of the Chi^2 test. Basically multiply everything by 100, and then do a normal Chi^2 test.\n\nYour degrees of freedom will be (6-1)(2-1) = 5
512136
Three types of approaches are, however, especially important. First, there\nare approaches that start out from the general notion of a law of nature, then define the ideas of necessary and sufficient nomological conditions, and, finally, employ the latter concepts to explain what it is for one state of affairs to cause another . Second, there are approaches that employ subjunctive conditionals in an attempt to give a counterfactual analysis of causation. Third, there are probabilistic approaches, where the central idea is that a cause must, in some way, make its effect more like Each of these three types of approaches faces difficulties specific to it. The attempt to analyze causation in terms of nomological conditions, for example, is hard pressed to provide any account of the direction of causation—a problem that quickly becomes evident when one notices that one state of affairs may be a nomologically sufficient condition for another either because the former is causally sufficient for the latter, or because, on the contrary, the latter is causally necessary for the former. In the case of counterfactual approaches to causation, a crucial problem is that traditional analyses of subjunctive conditionals employ causal notions. Alternative accounts have been proposed, involving similarity relations over possible worlds. But these alternative accounts are exposed to decisive objections. Finally, there are also specific problems for probabilistic accounts, two of which are especially important. First, probabilistic accounts have struggled to find an interpretation of their central claim—that causes must, in some way, make their effects more likely—that is not open to counterexamples. Second, probabilistic approaches to causation typically involve the very counterintuitive consequence that a completely deterministic world could not contain any causally related events.
204729
ok. here is one "HYPOTHESIS OF SOUND"!\n\nLet me know if you want another one! I got plenty.\n\nlol
732858
in testing the hypothesis > is the right side, < is the left side, not equal to is a two tailed test....for whatever test your using find the critical value...if it is greater than the alpha reject otherwise fail to reject
775942
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!
288137
(m)\n\nThe level of significance (alpha) is the probability of a Type I error that an investigator is willing to risk in rejecting a null hypothesis. Generally, it refers to the probability of the event occurring due to chance. If alpha = .01, it is likely that one time out of a hundred the event could occur due to chance. If you lower the significance level from .05 to .01, you decrease the probability of rejecting a true hypothesis but increase the probability of accepting a false hypothesis. A Type II error (beta) occurs when an investigator fails to accept the alternative hypothesis when in fact the alternative hypothesis was true. In other words, the null hypothesis was accepted when it was not true.
108416
none if they are false
248449
If you suspect that scores are not the same within two groups, you must place this statement in the alternative hypothesis.\n\nHo=scores are same\nHa=scores are not the same.\n\nBy claiming this this way, you can test it using the in formation obtained from the sample data and conclude whether or not data is sufficient to claim that you can reject the null hypothesis, and decide that scores are not the same. Always, you must place a new claim, a new suspicion in the alternative hypothesis, so that you can reject the null and conclude Ha, or you can not reject and conclude that you have not enough evidence to claim Ha.
584657
To make the explanation clearer, I'll assume p1 is the proportion of poeple supporting a party before a scandal and p2 is the proportion of people supporting a party after a scandal.\n\nThe null hypothesis is ALWAYS an equals\n\nH0: p1 = p2\n\nie you are saying there is no difference between the two proportions - that is the scandal has not affecting the proportion of people voting for the party - any fluctuation is just random variation rather than any significant difference.\n\nWhereas the alternate hypothesis can be greater, less or just not equal, depending on whether you want to test.\n\nH1: p1 > p2\n\nThe proportion of people voting for the party has significantly decreased after the scandal.\n\nH1: p1 < p2\n\nThe proportion of people voting for the party has significantly increased after the scandal (who knows why!)\n\nH1: p1 not equal p2\n\nThe proportion of people voting for the party has changed significantly after the scandal (up or down - we don't mind!)\n\nThe alternative hypothesis determines whether you have a one-sided or two-sided test and so what critical values you have.
180525
Hypothesis testing means assesing, with a degree of confidence, whether an hypothesis (an assumption) is correct or not. Applications for these techniques just fluorish once you have a grasp on it. For example, you can test whether your clients are satisfied or not, whether a given change in a service protocol changed the desired answer, etc.).\nA good introductory text is H.D Brunk Introduction to Mathematical Statistics ISBN: 0471008346 or (in spanish) INTRODUCCIÓN A LA ESTADÍSTICA MATEMÁTICA in Editorial Trillas; I'm sorry I don't have the ISBN for this).\nHOwever, any stastics text will do just fine.
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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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This is a simple two tailed test of the equality of means.\n\nLook this up in a stats book, but the Hypothesis that you are testing is Mean1=Mean2.
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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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A hypothesis test starts from the assumption that the null hypothesis is correct (c) and then you test whether the data is likely under this assumption.  If the data refutes the null hypothesis to the chosen significance level than you are said to have proven the alternative hypothesis to be likely.\n\n\nIs this for highschool homework?  If you need help I have a degree in statistics and can help you understand this stuff but if you just want someone to do your homework you should re-think this.  Trying to get to the end result without learning the process won't get you anywhere in life.  Even if you don't use this in your future career it is still worth learning.