what is relationship between experimental probabilities, theoretical probavililties, and the number of trials.
817262
Experimental probability is obtained by carrying out some trials to see what fraction of the time the event you are looking for occurs.  For example, if you want to know the probability of scoring a total of 8 when you throw a pair of dice, one way of doing it would be to throw a pair of dice five times and see how many times you get 8.  If it happened twice, you would say the experimental probability is \n2/5, or 0.4\nNot a very good result, though, too few trials.  If you did it 100 times, and 8 showed up 15 of those times, the experimental probability would be \n15/100, or 0.15\n\nTheoretical probability is found by considering how many equally likely outcomes there are, and in how many of those outcomes the event you are looking for occurs.  In the example I just gave, there are 36 different ways for the dice to land, and the total is 8 in 5 of those ways, so the theoretical probability is\n5/36, or 0.13888...\n\nWe expect a very small number of trials will not give a very good picture of the real probability.  Choose your answer from that.
767627
Quantitative methodologies... statistical analysis?   How many pages allowed on here... there are books and books about it... try reading literature on the above... infinite (well almost!) sources available on line, at the library and in book shops
650317
If you have a computer program that can compute binomial probabilities, you can use that.\n\nIf you are doing this by hand, however, I would suggest using the normal approximation to the binomial here.\n\nn = number of trials = 200\np = prob. of success = 1/6 if we consider getting a five to be a success.\n\nThen np = 200(1/6) = 33.33 and n(1-p) = 166.67 are both >= 5 or 10 (the cutoff is different for different classes) then the sample size is large enough to use the normal.  This binomial is approximately normal with mean = np = 33.33 and SD = sqrt(np(1-p)) = sqrt(200(1/6)(5/6)) = 5.27.\n\nNow for the continuity correction.  The rectangle over 40 goes from 39.5 to 40.5.  Since we are finding the probability that the number of 5's is at least 40, then we should start at 39.5 so that all of 40 is included.\n\nz = (39.5-33.33)/5.27 = 1.17\nArea to the right of 1.17 on the standard normal is 0.121.
154434
The problem cannot be attended to without knowing the total number of possibilities.\n\nhow many sections are there on the spinnner..\n\n\nhttp://www.futureaccountant.com/probability/\nhttp://www.schoolingkids.com/
885649
The bigger the sample size, the more accurate the estimate.\n\nSay I was estimating the percentage of red m&m's in a pack.\n\nIf I sampled 10 m&m's, I might I might find that the sample contains 1 red one. This could lead me to think that a pack contained 10% red m&m's.\n\nHowever, if I sampled 100 m&m's, I might find that the sample contains 15 red m&m's, leading me to think that a pack contains 15% red m&m's. This answer is impossible using the sample size of 10, as you can't get 1.5 m&m's of a particular colour.\n\nIf I sampled 1000 m&m's I might find that I have 143 red ones, leading me to think that the pack contains 14.3% red m&m's.\n\nSo, as you can see, the bigger the sample size, the closer the estimation comes to the actual answer.
254656
I'm assuming you are talking about a probability problem in Bayes' theory since you haven't specified what any of your symbols mean. This is why there are no responses.\nThe answer is actually quite clear. Draw three circles in a triple intersection for the events R, S and I and label it clearly. The diagram simplifies to a certain double intersection that motivates the result.
624712
This problem is solvable.\nProbability of event A is written P(A).\n\nP(A)= Total # of outcomes that correspond to event A (divided by -or- over) Total # of outcomes in sample\n\nAnswer:\nP(A) = 12/20 = 3/5
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1. probabilty of getting a value that extreme in truth if the null hypothesis is true\n2. 1. null hypothesis is false \n    2. null hypothesis is true and there's another factor at work\n3. conclusion: it is likely that you should reject the null hypothesis\n4. wtf
287369
Broadly, it means that the hypothesis was not sufficiently proven (or disproven). This could be because of flawed experimental design, weak or improper statistics, or incorrect interpretation of results.
867606
In order to measure probabilities, mathematicians have devised the following formula for finding the probability of an event.\n\nProbability Of An Event \nP(A) =    The Number Of Ways Event A Can Occur    \nThe Total Number Of Possible Outcomes  \n \nThe probability of event A is the number of ways event A can occur divided by the total number of possible outcomes.
1167496
I find interesting the use of probability to explain god. I mean, they use well known (and limited) data to explain how unlikely (even if possible) it is for life to start as a mere accident. Even though, these people seem to forget to apply the same logic and known data to explain god, if the experiment is a fair experiment, that is, using known data in both experiments, the probability that a being like god actually exists is 0. ZERO probability.
906595
C.\nThis is a binomial probability problem.\nThe binomial formula says \nP(r out of n) = (nCr)(p^r)(q^(n-r)), where n is the number of trials (here 6 rolls), r is the number of successes (here 0, 1, 2, and 3 twos), p is the probability of success (here 1/6), and q is the probability of failure (here 5/6)\nYou need to plug in 0, 1, 2, and 3 for "r" into this formula.  If you do, you get:\nP(0 twos) = .3348979767\nP(1 two) = .401877572\nP(2 twos) = .200938786\nP(4 twos) = .05358367627\nYou add up these answers to get your final answer, about .9913
979650
For any random variable X, if a is a positive number, it is clear that the probability that X will be larger than a goes to 0 as a grows without limit. This is because a probability distribution has integral 1, and therefore the area under the probability density curve (continuous distribution) to the right of a, or the sum of the probabilities for values of X larger than a (discrete distribution) have to go to 0 as a goes to infinity.\nHope this helps -
668585
This question is basically nonsense....\n\nBut, an experiment is a test or series of tests designed to isolate, quantify, and clarify whether a preconceived idea or theory is correct.\n\nHow to define that using mathematics...haven't a clue.
236313
Wouldn't the probability than 5 of the 38 thought it was exciting be\n\n38C5(.79^5)(.21^33) = 6.63x10^-18 : basically impossible?  Even if you added in the probabilities of 4, 3, 2, 1, and 0, it's still tiny.\n\nSo I would infer that either the 79% is wrong or the sample was not independently chosen.
587245
If you have n events where the number of outcomes for the first event is p1 and the number of outcomes for the second event is p2 and so on up to the nth event (pn), then the number of total ways the event can occur is\n\n(p1)(p2)(p3)...(pn).
272822
One cool way that I have seen it derived is based on Jayne's Formalism.\n\nAssume we sample process, and measure the mean and standard devation of the samples. There are many possible distributions that will produce those exact mean and standard deviation. \n\nJayne's Formalism chooses the distribution which maximizes the entropy of the distribuiton. This is viewed mathematically as the distribution that makes the minimum number of assumptions regarding the distribution.\n\nI always thought that was kinda cool.