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Answered 2013-05-23 12:48:07

Expected successes= Theoretical Probability · Trials

P(event) = Number of possible out comes divided by total number of possible

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The probability of the complement of an event, i.e. of the event not happening, is 1 minus the probability of the event.


theoretical probability is one half experimental probability is four tenths this is because to find theoretical probability you need to do number of outcomes you were looking for over the number of outcomes possible experimental probability is number of turns that were what you were looking for over the number of turns


To find the experimental probability of an event you carry out an experiment or trial a very large number of times. The experimental probability is the proportion of these in which the event occurs.


The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.


Read the introduction to probability and probability measures at StatLect.com


The answer depends on the probability of WHICH event you want to find!


The first step is to find a model that adequately represents the situation. You then apply reasoning based on the laws of science, along with some assumption regarding the model, to find out how likely a given outcome is. That value is its theoretical probability.


Odds against A = Probabillity against A / Probability for A Odds against A = (1 - Probabillity for A) / Probability for A 9.8 = (1 - Probabillity for A) / Probability for A 9.8 * Probability for A = 1 - Probability for A 10.8 * Probability for A = 1 Probability for A = 1 / 10.8 Probability for A = 0.0926


Odds of A to B in favour of an event states that for every A times an event occurs, the event does not occur B times. So, out of (A+B) trials, A are favourable to the event. that is, the probability of A is A/(A+B).


The theoretical probability that a tribute from 1,2,4 will win the Hunger Games is 25% chance. To find the experimental probability you would have to know which tributes won in the last 75 hunger games to know there chance of winning in the future.


what is the probability of P(4or6) as a fraction, decimal and a percent


EXPERIMENTAL PROBABILITYExperimental probability refers to the probability of an event occurring when an experiment was conducted.)In such a case, the probability of an event is being determined through an actual experiment. Mathematically,Experimental probability=Number of event occurrencesTotal number of trialsFor example, if a dice is rolled 6000 times and the number '5' occurs 990 times, then the experimental probability that '5' shows up on the dice is 990/6000 = 0.165.On the other hand, theoretical probability is determined by noting all the possible outcomes theoretically, and determining how likely the given outcome is. Mathematically,Theoretical probability=Number of favorable outcomesTotal number of outcomesFor example, the theoretical probability that the number '5' shows up on a dice when rolled is 1/6 = 0.167. This is because of the 6 possible outcomes (dice showing '1', '2', '3', '4', '5', '6'), only 1 outcome (dice showing '5') is favorable.As the number of trials keeps increasing, the experimental probability tends towards the theoretical probability. To see this, the number trials should be sufficiently large in number.Experimental probability is frequently used in research and experiments of social sciences, behavioral sciences, economics and medicine.In cases where the theoretical probability cannot be calculated, we need to rely on experimental probability.For example, to find out how effective a given cure for a pathogen in mice is, we simply take a number of mice with the pathogen and inject our cure.We then find out how many mice were cured and this would give us the experimental probability that a mouse is cured to be the ratio of number of mice cured to the total number of mice tested.In this case, it is not possible to calculate the theoretical probability. We can then extend this experimental probability to all mice.It should be noted that in order for experimental probability to be meaningful in research, the sample size must be sufficiently large.In our above example, if we test our cure on 3 mice and all of these are cured, then the experimental probability that a mouse is cured is 1. However, the sample size is too small to conclude that the cure works in 100% of the cases.R\


odds"The odds against an event is a ratio of the probability that the event will fail to occur (failure) to the probability that the event will occur (success). To find odds you must first know or determine the probability of success and the probability of failure.Odds against event = P(event fails to occur)/P(event occurs) = P(failure)/P(success)The odds in favor of an event are expressed as a ratio of the probability that the event will occur to the probability that the event will fail to occur.Odds in favor of event = P(event occurs)/P(event fails to occur) = P(success)/P(failure)"Allen R. Angel, Christine D. Abbott, Dennis C. Runde. A Survey of Mathematics with Applications. Pearson Custom Publishing 2009. Pages 286-288.



When you can find a model that satisfactorily captures the scientific laws behind all the possible outcomes of the trial.


If we assume that the probability of an event occurring is 1 in 4 and that the event occurs to each individual independently, then the probability of the event occurring to one individual is 0.3955. In order to find this probability, we can make a random variable X which follows a Binomial distribution with 5 trials and probability of success 0.25. This makes sense because each trial is independent, the probability of success stays constant for each trial, and there are only two outcomes for each trial. Now you can find the probability by plugging into the probability mass function of the binomial distribution.


Suppose there is an event A and the probability of A happening is Pr(A). Then the complementary event is that A does not happen or that "not-A" happens: this is often denoted by A'.Then Pr(A') = 1 - Pr(A).Suppose there is an event A and the probability of A happening is Pr(A). Then the complementary event is that A does not happen or that "not-A" happens: this is often denoted by A'.Then Pr(A') = 1 - Pr(A).Suppose there is an event A and the probability of A happening is Pr(A). Then the complementary event is that A does not happen or that "not-A" happens: this is often denoted by A'.Then Pr(A') = 1 - Pr(A).Suppose there is an event A and the probability of A happening is Pr(A). Then the complementary event is that A does not happen or that "not-A" happens: this is often denoted by A'.Then Pr(A') = 1 - Pr(A).


It is not necessary. Sometimes, though, it is easier to find the probability of the complement and subtract that probability from 1.


If the events are independent then you can multiply the individual probabilities. But if they are not, you have to use conditional probabilities.


Experimental probability is calculated by taking the data produced from a performed experiment and calculating probability from that data. An example would be flipping a coin. The theoretical probability of landing on heads is 50%, .5 or 1/2, as is the theoretical probability of landing on tails. If during an experiment, however, a coin is flipped 100 times and lands on heads 60 times and tails 40 times, the experimental probability for this experiment for landing on heads is 60%, .6 or 6/10. The experimental probability of landing on tails would be 40%, .4, or 6/10.


The formula for probability is as follows:Probability = Total number of favorable cases / total number of casesIt always lies between 0 and 1 and has no unit.


There a formula: Probability = No. of Cases in favour / Total no. of cases


In experimental probability the probabilities of the outcomes are calculated as the proportion of "successful" outcomes in repeated trials. In theoretical probability these are calculated on the basis of laws of science being applied to a model of the experiment. For example, to find the probability of rolling a six on a standard die, you could roll the die many times (N) and count the number times that it comes up 6 (n). The experimental probability is n/N. The theoretical approach would be to work from the principle that each outcome was equally likely - since it is a fair die - and since the total probability must be 1, the probability of any one face must be 1/6. The second method will only work if there is a good mathematical model.


P(A given B)*P(B)=P(A and B), where event A is dependent on event B. Finding the probability of an independent event really depends on the situation (dart throwing, coin flipping, even Schrodinger's cat...).


To find the probability that an event will not occur, you work out the probability that it will occur, and then take this number away from 1. For example, the probability of not rolling two 6s in a row can be worked out the following way:The probability of rolling two 6s in a row is 1/6 x 1/6 = 1/36Thus the probability of not rolling two 6s in a row is 1 - 1/36=35/36.