What is a good statistical hypothesis?
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Go to the local college campus and advertise your lil but off. You can have an average age group, or a centralize location, or gender vs gender. The factors are endless. For example take a basic Rubik's cube and scramble it. How long will it take the the individual to give up and hand it back?\nPeople who are more mechanically inclined will generally fight with it more than most. Hope this sparks some imagination in the right direction. Have fun and good luck.
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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.
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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
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A hypothesis is an educated guess about a scientific problem.  A theory is what you get when your hypothesis has evidence to back it up.
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factual evidence, if you are always saying i think this and i think that, then there is no substance to your argument... you need to have statistics and facts that prove your argument
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I'm assuming that you mean introductory statistics.  This online textbook is very good, it makes good use of hyperlinks and also provides exercises and answers for each chapter.  It also has links to useful sites and books for the various topics.  \nhttp://davidmlane.com/hyperstat/\nHope this is helpful :-)
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Depends on the sample size and population and things.  Generally, a z-table is good enough.  If the area outside is .0002, then that's around 3.7 sigmas.
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not C or B, so it come to A, or D, a pvalue between 0.01-0.05 is generally considered strong evidence for alternetive hupothothes and against null hypotheses, A says same thing as choice C so D is the right anwer
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Good character\nIntelligence\nStrength
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Last poster is right but in statistics, alpha is the level of significance or the probability of rejecting the null hypothesis when the null hypothesis is true.  It's kind of like saying, "how sure am I that the assumption I'm making is going to be correct?"  Say that you wanted to be 95% sure that the price of wheat tomorrow will be $1 a bushel. Your alpha would be 5% or 0.05
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I just answered this one. :)\n \n 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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There are several good statistics textbooks free online, and free software, too.  Be sure to check out R, the free statistics software package.
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procedure in science....the steps taken to test a hypothesis
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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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Engineering Statistics is based on the fact that everything can be explained as either part of a larger population or it is not.\nIf it is part of the population, the hypothesis test is true. If it is not part of the population the hypothesis is false and there must be an assignable cause.\n\nEngineering is always looking for inprovement. Using stats helps to explain variables and interactions affecting the outcome of an event.\n\nThere are other tools to accomplish this task, i.e. Run Charts, Scatter Diagrams, Taguchi Experiments, Design of Experiments, XbarR, Value Mapping ect.\n\nRead up on Deming, Joiner, Juran\n\nLook at the following Gurus list:
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Great idea.  What is your hypothesis?  What experiment will you do?  What data will you collect?  How will you interpret it?
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b, of course.\n\nif one has some preconceived notions, statistics can be used as objective data to shed factual light on the subject.  doing so will either support your notions (choice a) or discredit them.  \n\nanybody, when faced with objective and factual information that discredits their ideas, should reconsider those ideas or incorporate the new facts into their philosophy.  this is the cornerstone of open and honest discussion.\n\nstatistics as "scientific data" are meaningless unless one adheres to the fundamental premise of science, which is that experiment is the sole judge of truth.\n\nit is reasonable to use statistical data to support your ideas in a debate, as long as you remain open to reconsidering your position in the light of new information!  \n\nanyone can (and many do) cherry-pick or manipulate statistics to support a particular idea, when there is plenty of other statistical data that they pretend doesn't exist or is irrelevant.  doing so is intellectually dishonest.