What role does variabilty play in understanding the importance of the p-value?
476727
When carrying out a test - we cannot actually "prove" results (science can only observe data) - we can just give the probability that our result was due to variability (the p-value).  The lower the p-value, the more likely our result is due to a significant difference rather than variability.\n\nFor example suppose we were testing the population heights of males.\n\nSuppose our hypothesis is that they have a height of 5 foot 10 inches on average.\n\nWe then take our sample, suppose this gives a mean height of 6 foot.\n\nThere is two inches difference - is this because we just happened to pick a group of taller people (say basketball players) from the population or is it that our results indicate that the population mean is not 5 foot 10?\n\nThe p-value is the probability of getting a "more extreme" result than our sample result.\n\nSuppose we have a p-value of 2% - this means that there is a probability that 2% of the population is greater than 6 foot - wow that's small - and pretty unlikely - so it seems reasonable (though it's not conclusive - there's still a 2% chance we've messed it up due to the inherent variability of random samples) that our result is different from our assumption of 5 foot 10.\n\nP-values less than 5% are considered to be "significantly different" but obviously there is a 5% chance our sample result is simply due to the variability of picking a random sample (we might have just happened to pick basketball players - as mentioned above).\n\nHence, when people repeat clinical tests they may get different results depending on the sample.  Hence, the need for larger samples and repeated independent tests to be "sure" - but hey we never can be completely sure!! (for example I could pick a couple of million people who have never won the lottery and conclude that no-one ever does win...)\n\nHope this helps.
308805
I predict the value will increase without bound. One factor in  any potential success is the way you integrate around the pole. There could be a strong singularity!
508493
I've found that the Wikipedia articles on probability distributions are top notch and contain enough information for you to solve this problem on your own.\n\nHint: solve the algebraic expressions that relate mean and variance so you can express p or n as algebraic relationship of one another and then do some substitions.\n\nAt first I thought you needed to know p or n up-front however I realize that you have all the info you need to solve this problem algebraically.\n\nGood luck with your homework.
478561
I don't remember N value, but I know that P value is the probability that something is done by "chance".  In other words, I cannot "prove" that I am a psychic, but I can do things that point farther and farther away from me 'cheating" or "just guessing".  The farther one gets from something being "chance" the smaller the P value is.  In Statistics, P-value is only considered "significant" if it is below .05 (or less than possibly happening one in 20 times).
85296
SPSS\nWith your data entered, go to ANALYZE > GENERAL LINEAR MODEL > UNIVARIATE \nEnter dependent variable in Dependent List \nEnter independent variables in Factor \nContinue – OK \nOutput:\n(A) Descriptives (N, Mean, Std. Dev., etc) \nThis will give you a table. On the right hand side, the significances will be listed, along with the F values. On the far left side, under the column “source,” your variables will be listed. The corresponding F values and significances are for main effects and interaction effects. If the significance reading is less than .05, you are able to reject the null hypothesis.
1008113
The Russian mathematician P. L. Chebyshev (1821- 1894) discovered that the fraction of observations falling between two distinct values, whose differences from the mean have the same absolute value, is related to the variance of the population. Chebyshev's Theorem gives a conservative estimate to the above percentage.\n\nFor any population or sample, at least (1 - (1 / k)2) of the observations in the data set fall within k standard deviations of the mean, where k ³ 1.\n\nUsing the concept of z scores, we can restate Chebyshev's Theorem to say that for any population or sample, the proportion of all observations, whose z score has an absolute value less than or equal to k, is no less than \n(1 - (1 / k2 )). For k = 1, this theorem states that the fraction of all observations having a z score between -1 and 1 is (1 - (1 / 1))2 = 0; of course, this is not a very helpful statement. But for k ³ 1, Chebyshev's Theorem provides a lower bound to the proportion of measurements that are within a certain number of standard deviations from the mean. This lower bound estimate can be very helpful when the distribution of a particular population is unknown or mathematically intractable.
256844
It allows you to compare different sets of data.\nSo one day when you have lots of different info, you can use these statistical calculations to make a decision that suits you.\n\nExample: Say you want to invest in stock. The mean gives you what you can expect to earn on the stock. The variance is a measure of how risky the stock is- the higher the variance, the more risky the stock. Usually a higher variance means you can expect a greater return(mean). The trick is to find the balance between maximising return while minimising risk.
336426
The output should show you the p-value and significance.  It is the level of significance that is important-it should be at least below 0.05.   This information is found in a box at the bottom of the report-ther rest of it is just analysis of individual variance, but the really important info is the overall significance.  Tukey's test is one of the post-hoc tests, along with Bonferonni's, Scheffe's, etc.  These tell you the strength of the effect and where it's coming from, but are again not as important as that magic number, the significance of the Anova.  The post-hoc tests are up to individual choice, so you could use any of them.
362053
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.
377891
I think statistics has a higher chance of being relevant. It's usual to be able to analyze studies in any subject.
884757
I assume that you mean for p, the only parameter.\n\nIt's k/n where k = number of successes in n trials.
558645
These are my steps; it might be different for different people.\n\nCritical value approach:\n\n1. State null and alternative hypothesis.\n2. Compute the test statistic.\n3. Find the critical value and rejection region.\n4. Make you conclusion.\n\nP-value approach:\n\n1. State null and alternative hypothesis.\n2. Compute the test statistic.\n3. Find the p-value.\n4. Make your conclusion.
353290
You are close.\n\nIn the T-test, you are evaluating the difference of the means between two samples normalized to the pooled standard errors of the two samples.\n\nIf mc is your control mean and mt is your test mean, then the T-value is the number of pooled standard errors difference mt is away from mc.\n\nYou can then look up this value in a table of T-values to find the probability of difference as a p-value for assessing the significance.  The "rote" significance level is 95% or a p-value of 0.05 (100% - 95%) which is a absolute T-value of 1.96 or more.
320722
The central limit theorem still applies.\n\nWhen variables are correlated a principal component analysis will find the independent variables for which the CLT applies.
129909
You might find the following informative:\nhttp://www2.mtroyal.ab.ca/~igilborn/admn1382/regression/theory/part_b.htm
31321
it is directly propotional(when efficiencies r equal)\nactual value may vary based on efficiency i9j al;l other cases
1095989
I'm not sure if you're referring to significant values, but basically it can tell you correct a value is to its true value. This plays an important role in economics and trade... its like how do we know what is 1Kg or 1 second, is the American 1Kg the same as the european 1Kg...that kind of stuff... They usually express those figures in terms of uncertainty, how uncertain can we be that the scientific measured value is the true value. \n\nIn problem solving, if we round of values early in a problem those errors accumulate in the final value... its best to round off at the end. Rule of thumb is to work to one more decimal place than you would eventually require.\n\nHope that helps.