Q: Is it possible for two separate groups to conduct the same experiment to determine an experimental probability and get different results?

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The experimental probability of anything cannot be answered without doing it, because that is what experimental probability is - the probability that results from conducting an experiment, a posteri. This is different than theoretical probability, which can be computed a priori. For instance, the theoretical probability of rolling a 3 is 1 in 6, or about 0.1667, but the experimental probability changes every time you run the experiment

Conduct the following experiment: Roll a number cube 50 times. Count the number of times you roll a 2. Divide that number by 50. That is the experimental probability. The answer that I might get may well be different to yours. And if you do you experiment another time, the answer is likely to be different.

Each series of experiments is likely to give a slightly different answers. You will need to conduct the experiment and countthe number of times you got a 6 (= n6); andthe total number of times the experiment was conducted (= N).Then, the required probability is (N - n6)/N. As you increase N, the experimental probability will become more accurate.

There are many different types of mathematical experiments in math, but the most easy one I can think of would be the Experimental Probability. Example: Flipping a coin and recording your answers to see the actual probability of landing on heads or tails.

You are asking a question about experimental probability. The problem with that type of question is that the answer is different each time you run the experiment. That's why we call it experimental probability. The outcome will be different each time you run the experiment.This is different than theoretical probability, where you can compute a probability based on some a priori knowledge of the conditions of the experiment. For instance, if you asked me what the probability of throwing a 3 or a 4 on a 12 number die, I could easily compute that as 2 in 12, or 1 in 6, or about 0.1667. Even multiple experiments can be predicted. For instance, if you asked me what was the probability of throwing a 3 or a 4 on a 12 number die three times in a row, I could also easily compute that as (2 in 12)3 or about 0.004630.Alas, experimental and theoretical probability part company and one does not assure the other, unless you run a very large number of tests but, even then, you only do what we call approachthe theoretical results with the experimental outcome.

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The experimental probability of anything cannot be answered without doing it, because that is what experimental probability is - the probability that results from conducting an experiment, a posteri. This is different than theoretical probability, which can be computed a priori. For instance, the theoretical probability of rolling a 3 is 1 in 6, or about 0.1667, but the experimental probability changes every time you run the experiment

The experimental probability of anything cannot be answered without doing it, because that is what experimental probability is - the probability that results from conducting an experiment, a posteri. This is different than theoretical probability, which can be computed a priori. For instance, the theoretical probability of rolling an even number is 3 in 6, or 1 in 2, or 0.5, but the experimental probability changes every time you run the experiment.

The theoretical model does not accurately reflect the experiment.

Conduct the following experiment: Roll a number cube 50 times. Count the number of times you roll a 2. Divide that number by 50. That is the experimental probability. The answer that I might get may well be different to yours. And if you do you experiment another time, the answer is likely to be different.

Each series of experiments is likely to give a slightly different answers. You will need to conduct the experiment and countthe number of times you got a 6 (= n6); andthe total number of times the experiment was conducted (= N).Then, the required probability is (N - n6)/N. As you increase N, the experimental probability will become more accurate.

There are many different types of mathematical experiments in math, but the most easy one I can think of would be the Experimental Probability. Example: Flipping a coin and recording your answers to see the actual probability of landing on heads or tails.

yes because a quarter has 2 sides but flipping it you dont have a 100%chance if it lands on the same side

experimental and control

You are asking a question about experimental probability. The problem with that type of question is that the answer is different each time you run the experiment. That's why we call it experimental probability. The outcome will be different each time you run the experiment.This is different than theoretical probability, where you can compute a probability based on some a priori knowledge of the conditions of the experiment. For instance, if you asked me what the probability of throwing a 3 or a 4 on a 12 number die, I could easily compute that as 2 in 12, or 1 in 6, or about 0.1667. Even multiple experiments can be predicted. For instance, if you asked me what was the probability of throwing a 3 or a 4 on a 12 number die three times in a row, I could also easily compute that as (2 in 12)3 or about 0.004630.Alas, experimental and theoretical probability part company and one does not assure the other, unless you run a very large number of tests but, even then, you only do what we call approachthe theoretical results with the experimental outcome.

There are a number of different things which can improve the estimate:select an appropriate estimation method,repeat the experiment more times,Improve the accuracy of your measurement,ensure that other variables are properly controlled.

A control sample is the experiment under regular conditions. An experimental sample is the experiment in which different variables are changed.

Accounting for errors in an experiment will determine the validity and reliability to the experiment. This, in turn, will either support the experimental results by accepting the null hypothesis or to discard the experimental results by rejecting the null hypothesis