what does the term confidence mean when referring to a confidence interval for a statistical test.?
343892
how confident you are that the result your statistical test generates reflects the result you would get if instead of a sample, you tested the entire population.
760097
Try this: http://en.wikipedia.org/wiki/Confidence_interval
282565
"z-test" usually refers to normal distribution. Which means \n1) That the population is normal \n2) That the population parameters (mean and variance) are known\nthen you can test against a Z by letting z=(x-mu)/sgima, that distributes normal-standard.\n\nNow, if you don't know the population parameters, then you go the t way (Correctly known as STudent-t to honor its inventor)\n\nNow, t aproaches z as sample size grows. In practical terms, it means that ,for sample sizes below 100 you use t and for larger samples you use normal
399013
As it happens, I just got back from a test on statistics. We covered that very question. \n\nHere goes:\nthe sample size is dependent on how confident you want to be about your results. In the following formula, then Z is the confidence level. Use 1.65 for 90% confidence level, 1.96 for 95%, 2,33 for 98% and 2.58 for 99%. \nYou also need to know the standard deviation, which should be given. and the error allowable, for instance 3%, or 5% or such. That number goes into the formula as E. \n\nso number n equals = (Z * standard deviation/E)^2
662741
In basic statistical hypothesis testing, the P-value represents the probability of encountering a result more extreme than the value that we've observed in our testing.\n\nAs an example - suppose we had conducted a properly designed experiment, and discovered that the mean score of our sample was 103.5. We would evaluate this finding of 103.5 by comparing it to a known "sampling distribution" - a distribution of sample means of size n that was randomly drawn from a population with (for the sake of this example) a mean of 100 and a standard deviation of 1.75.\n\nWhat we want to do is evaluate the likelihood that a sample of size n - randomly drawn from the population - would have a mean as extreme as 103.5.\n\nWe begin with the assumption that the null hypothesis is true - that our sample belongs to the population - and that the deviation of 3.5 from the population mean of 100 is simply due to random error.\n\nIn this situation, the appropriate statistical tool for evaluating the probability of observing a sample mean as extreme as 103.5 is the t-test. We begin by "standardizing" the raw score of 103.5 by converting it to a deviation score and then dividing that deviation score by the standard deviation of the sampling distribution.\n\nThe formula is simply  (103.5 - 100.0) / 1.75  =  2.0\n\nThis means that our sample mean of 103.5 is 2.0 standard deviation units above the mean of the sampling distribution. Now we need to find out the probability of encountering a sample that is 2 standard deviation units from the mean. \n\nBecause the sampling distribution is assumed to be approximately normally distributed, we can refer to the z-table (reproduced in any statistic text), and discover that only 2.28 percent of all the scores in a normal distribution fall above 2 standard deviation units above the mean. The proper interpretation of this result is that the probability of observing a sample this extreme from a population with a mean of 100 and a standard deviation of 1.75 is 2.28% or less.\n\nP-values are used in conjunction with critical values - pre-determined probabilities that we feel are so unlikely to have occurred by chance, that we are comfortable concluding that results this extreme could not have been selected from the population (thereby rejecting the null hypothesis that the sample was selected from the population).\n\nThe common critical values are .05 and .01, meaning, in each case, that we'll reject the null hypothesis if our finding is so extreme that it would only be expected to happen by chance less than 5 percent of the time - or in the case of .01, less than 1 percent of the time.\n\nReferring back to our finding - we concluded that a result of 103.5 would only happen 2.28 percent of the time or less - if our pre-selected critical value had been .05 (5 percent), we would reject the null hypothesis, and conclude that our sample was not selected from the population. If the critical value had been 1 percent, because our finding happens 2.28 percent of the time, we would fail to reject the null hypothesis.\n\nMan, I hope this made some sense!
199982
In statistics, the standard error of a measurement, value or quantity is the standard deviation of the process by which it was generated, after adjusting for sample size. In other words the standard error is the standard deviation of the sample mean.\n\nStandard errors provide simple measures of uncertainty in a value and are often used because:\n\n    * If the standard error of several individual quantities is known then the standard error of some function of the quantities can be easily calculated in many cases;\n    * Where the probability distribution of the value is known, they can be used to calculate an exact confidence interval; and\n    * Where the probability distribution is unknown, relationships like Chebyshev's or the Vysochanskiï-Petunin inequality can be used to calculate a conservative confidence interval.
729266
Yes.  This would require the use of a t confidence interval.  That is because the sample is small, the population is normal, and the population standard deviation is unknown.  You would have to compute the mean and the standard deviation of the six values to determine the confidence limits.\n\nedit: BTW, the mean is 81.98 and the standard deviation is 4.18.  The confidence interval I get is (77.18, 86.87)
486571
I can do two - \n\nSystematic: inaccuracies which persist throughout the entire experiment as a result of methodology.\n\nRandom: statistical fluctuations in the measured data due to the precision limitations of the measurement device.\n\nI'll get back to ya on the other one...
861521
you would need to look at table for t values.\nyou are looking for a 95% confidence level, so you would divide .5 (which is 1-.95) by 2 and get .025 that is the column you find the value for t.\nyour n number, the row for the t number, is 15-1 or 14 and 12-1 or 11.\nLook at the table IV in colum t=0,025 and row 14 write down the number. that number is the t value for the first equation.\nLook at table IV in column t=0.025 and row 11, that is your t value for the second equation.\nNow that you have the t values you can plug them into the formula for confidence interval.\n\nGood luck
766555
Your 5-day mean, while it has a smaller interval, is less accurate.  Your 10-day mean, while it has a larger interval, is more accurate.  To better understand this without math, its best to understand that as your number of samples goes up, your data will "normalize", or approach a consistent distribution.  While it is impossible to say within what finite range, with 100% accuracy, your mean will fall, your interval will become more and more accurate as your number of samples increases.  So, as per predicting the results after tomorrow's data is entered, your 10-day mean is more accurate.  Understand also, though, that neither mean is a tool for predicting the results of tomorrow ALONE, rather they are tools of predicting the interval after tomorrow's data is ADDED to the data ALREADY COLLECTED.\n\nSteve
387498
That means a survey of a population of similar cases to find out what percentage of cases exhibit one characteristic versus another. It's a statistical kind of attempt to find out what the average is and how much different cases might deviate from the average case. In mathematical statistics they refer to these parameters of the population as the mean and standard deviation.
332290
Generally it is the standard deviation, or range of typical answers.  The mean usually is the center of the bars, but not always.
213752
It's a way of expressing something in unitless terms.  Very important for statistics and probability.  Also useful for comparing two numbers of the same unit.
408294
in statistics what you do is take a small sample, do the tests on the small sample and then generalize the results to the whole population.\n\nHere you took a small sample, found the mean to be 0.68\n\nBut the mean in the whole population will not be always equal to 0.68 . It can be 0.68, but it can lie anywhere around it to. \n\nHence, we have our confidence intervals around 0.68\n\nSo, when we say that the confidence intervals are (.63692, .72308), it means that we are 95% sure that the whole population mean will lie between 0.63692 seconds and .72308 seconds. \n\nSince 0.75 is not in that range, the chance of the population mean being 0.75 is less than 5% or negligible. \n\nI hope that helps.
9419
A statistical t-test measures the mean difference between two groups.  So it tells you if, on average, one group differs from another group along some dimension.\n\nt-stat is the actual value of the t-test.  It is equal to the mean difference divided by the pooled standard deviation between the two groups.  \n\nt-critical is what you compare the t-stat to in order to decide if it is statistically significant. You usually look this up in a table. So, if the t-stat is bigger than the t-crit, we conclude that there is a difference, on average, between the two groups in the population.
446411
Usually when  you pull a sample, you know the size of the sample you pulled.  Otherwise, how would you calculate any of the test statistics much less the margin of error?
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.