The probability that a flipped coin has a probability of 0.5 is theoretical in that it assumes the existence of a perfect coin. The same can be said of the probabilities of the spots appearing on a single tossed die which requires the existence of a perfect die. Here's an example. Consider tossing a coin twice to see what comes up. It could be tail, head, or head tail, or tail, tail or head, head. The theoretical probability of two heads is one in four. In general, theoretical probability is the ratio of the number of times a possible outcome can occur in a given event to the number of times that event occurs.
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There are five letters, and two of them are s's. The theoretical probability of choosing an s would be 2 out of 5.2/5 or 40%
The term is probability (theoretical probability), or how likely a given event is to occur.
you choose the independent variable, for example to see if aspirin helps bee stings, you choose whether or not to put it on. Aspirin is the independent variable, probability is not involved.
2 numbers. few probabilities. Lets see. Number 1 probability: if your given numbers are this for example, 2 and 7. What is the probability of picking out 7? the Numerator is how many of that number is in the group. They are asking for 7? so how many 7's are in the group? 1. Then the denominator is how many numbers are in the group. There are 2 numbers in the group. so the probability of picking out a 7 would be 1/2. get it? if there were two 7's, then the probability would be 2/2 or 1. I hope I helped.
Sometimes you can make a practical assumption about the probability of something occurring by considering all the possible outcomes. For example, a coin only has two sides so the probability of it landing on heads (assuming that the coin is "fair") is 1/2. Similarly, a die only has six sides so the probability of it landing on a 4 is 1/6. At other times it may not be possible to make any assumptions about the possible outcomes. In those situations you may have to estimate the probability by measuring how many "successes" you get as a proportion of how many "trials". Say, for example, you want to estimate the probability of a yellow truck going down the road. You could sit by the side of the road for a day and measure the number of yellow trucks that pass by (successes) as a proportion of all traffic (trials).