in statistics, what is decisional error?
349744
I'm assuming you're referring to Type I and Type II error, which has to do with hypothesis testing.  In hypothesis testing, we're interested in whether or not a given scenario is consistent or not with a null hypothesis.  For example, say that past data states that the average income for an american household is $35k a year (this is the null hypothesis).  We think that number has gone up significantly since the last study.  So, we collect a bunch of data, to see if we're right.  In order to compare the two, you need to calculate a test statistic, and in order to limit your chance of making a mistake, you select an alpha level (which is usually 0.05 in most statistics textbooks).  This alpha level is your probability of making a type I error, which is when you reject the null hypothesis when it's actually true.  Meaning, your data told you that the mean income had gone up significantly, but by random chance you just happened to sample higher than average income families, and it actually hadn't gone up at all.  The other type of error is Type II error, which is when you fail to reject the null hypothesis when it's actually false.  In terms of the example:  We happened to collect data that said that the mean income hadn't changed, but it actually has.  This error is related to a statistic called 'power' - the higher the power of your test, the less likely you'll make a type II error.  However, it's generally impossible to increase the power of your test without also increasing the alpha, or the probability you'll make a type I error, and most of the time, making a type I error is much worse than making a type II, so most statisticians focus on just avoiding type I errors.
442828
total chances (# of times a player in the field plays a ball) when the numbers of errors is divided by total chances fielding pct is obtained
820522
"Decision control"? I hope, you mean "flow control"... the decisions, hopefully, are still under the programmer's control.\nI hope, you don't expect ALL the flow control structures explained... They are fairly self-explanatory, and there are tons of books  on the subject.\nI'll explain a couple to you here:\n- goto keyword is used to implement non-sequentual execution paths - like conditional statements or loops.\nfor example:\nL1: cin>>s;\n      if cin.eof() goto L2;\n      cout << s;\n      goto L1;\n\n- Long jump is similar to goto, but can be used to implement "jumps" accross code segments. Useful to implement function calls or so called exceptions.\nFor example:\nstring error_message = 0;\nvoid *ret = 0;\nL1: if (!error_message) longjmp (L2);\nabort (error_message);\n...\nL3: cin >> s;\nif cin.bad() goto L4;\ncout<<s;\ngoto L3;\nL4: error_message = "Read error"; \nlongjmp(L1);
1097657
i agree partially with you. Errors are part and parcel of any game say it tennis or football or name any game. LBW decisions cannot be decided by third umpire with the camera angles. The umpire in the field is the best to give the decisions. It is a error ridden game, you may be unlucky in getting a decision. The same gayle who should be out on 0, went on to make 72.\n\nfinally it evens out. If third umpire has to give all the decisions, it would slow down the temper of the game. For runout decision, it is fine. For LBW, i cannot accept with you. Sorry
929935
variations matter greatly in interpreting data.  Something might seem like it is returning significantly different results than the control but stat evaluation may show that the variations are minimal at best
819203
Tracking error is the deviation of a funds returns from its benchmark.  Information ratio, when taken over several time periods, demonstrates is the managers ability to beat the benchmark is improving or decreasing.  This really differs from alpha in the sense that alpha is showing the risk adjusted return.
1156399
This is a good question even for a statistician.\nThe problem is your set is small and I do not know what the deviation between samples is, however the repeatability and reproducibility information can bind the window \nthrough which the third value will fall through or perhaps even remain in.  \n\nLet me know.
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.
1100234
There are two types of errors. One human error another instrumental error. \nWe know the accuracy of measurement of instruments. \n\nAny error in the result must be within this instrumetal error.\n\nHuman error must be eliminated in total. \n\nThis can be done only by repating the experiment.\n\nAny result which is not within the instrumetal error must be totaly rejected and we find the mean value of the results which are within the instrumental errors
750159
Type 1 error:\n\nRejection of a true hypothesis\n\nType 2 error:\n\nFailure to reject a false hypothesis
259386
http://en.wikipedia.org/wiki/Logical_fallacy
1061162
it is a conditional probablility problem.  if you select a defective on your first pick (3/12), there are only 11 left, 9 of which are not defective.  so the probablility of not picking a defective on your second pick given that you picked a defective the first tome is 9/11.
958234
maybe there is something wrong with test to had conducted that yields a wrong result.\n\ni'm not saying that your test is not correct.\n\nIt's just that it is not impossible that we commit errors in testing.\n\nremember the type I and II error in hypothesis testing.\n\n\nmaube the calculated and tabular value of your test has almost the same value.\nso it may suddenly falls under or above it.\n\n\nthat's why\ni think\nhoping it helps you
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.
124804
For an uncertainty x of 0.01 (which your 99% accuracy would be) you need 1/x^2 samples which would be 10000.  This is true for a large population (>>10000).  I believe you still need that many for 20000 population but am only 99% sure.\nSource: Best recollection of a stat course 50 years ago.
739621
Statistics can be manipulated to prove or disprove anything you want.  If you want to trust them, you will have to trust whatever entity put them together or look at the same type of statistics from several reliable sources.
569724
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