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Probability determines likely outcomes, not what will happen every time. For instance, if you tossed a coin, the probabilty of coming up heads or tails is even, but there is also a vanishingly small possibility that it wil land on its edge.
If you only tossed the coin once, and it came up heads, you could not infer from that result that "if I toss a coin, it will come up heads." The next 10 tosses of the coin might come up heads, but if the experiment is run enough times, a roughly equal number of heads and tails would be observed.
This is true of any probability experiment. In biological experiments especially, because we are dealing with the unpredictability of living organisms, a watershed has to be determined, below which we can say with reasonable certainty that the probability of a particular outcome is less than, say 0.5%.
If you imagine a bell, the edges flare out, and it rises steeply to a shallow curve at the top. If we place the results of our repeated experiments under that bell, most will fit somewhere under the shallow part of the curve, with the results falling of rapidly to approaching 0 as the values move down to the flare. With just 1 or 2 results, it would be easy to get a distorted picture of a likely outcome.
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Why does the experimental probability and theoretical probability differ?
The theoretical probability of event X is the proportion of event X out of N trials as N tends to infinity. Thus, if you flip a coin many times, you get closer and closer to 1/2 being H and 1/2 being T. This differs from experiment because all experiments take place with a finite number of trials.
How many trials are needed to view statistically significant results?
The answer depends on the probability of whatever it is that you are trying to observe and its variability. If the probability of a particular outcome is very high then you will need a lot of trials before you get one where the outcome does not occur. Conversely, a rare event will also require many trials. If there is a lot of random variation in the outcome of the trials, you will need more trials before you can be confident of the accuracy of any estimates.
How many times would you expect draw the ace of spades if you have 13 cards in each of four suits and draw one card?
The probability of drawing the Ace of Spades from a standard deck of 52 cards is 1 in 52, or about 0.01923. However, the number of times you can expect to draw it depends on random statistics. If you tested this a large number of times, shuffling the deck each time, you would expect about 1 out of every 52 trials to be the Ace of Spades, but that would only be in the long run, say for thousands and thousands of trials, and even then, it would not be exact. This is the difference between theoretical probability and experimental probability. Theoretical probability is based on pure statistics and the arrangement of the test. All you can say is that, for an infinite number of trials, you would expect 1 out of 52. In the case of experimental probability, you are limited by the number of trials that you can perform. Lets say you ran 10,000 trials. Theoretically, you would expect to draw the Ace of Spades about 192 times. In practice, you would have a range of results.
What is an experiment in math?
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.
What is scientific probability?
It is a number expressing the likelihood of the occurrence of a given event, especially a fraction expressing how many times the event will happen in a given number of tests or experiments.
Why does the experimental probability and theoretical probability differ?
The theoretical probability of event X is the proportion of event X out of N trials as N tends to infinity. Thus, if you flip a coin many times, you get closer and closer to 1/2 being H and 1/2 being T. This differs from experiment because all experiments take place with a finite number of trials.
Why is it important to carry out a large number of trials in determining probability?
If you only carry out a few trials, then how can you know how many times a particular situation will occur? One has to do a lot of trials in order to determine how many times that situation will happen so he can conclude the probability he's looking for.
What is a Definition of trials in the scientific method?
It means how many times it is used in or thought of in an expieriment
How many trials are needed to view statistically significant results?
The answer depends on the probability of whatever it is that you are trying to observe and its variability. If the probability of a particular outcome is very high then you will need a lot of trials before you get one where the outcome does not occur. Conversely, a rare event will also require many trials. If there is a lot of random variation in the outcome of the trials, you will need more trials before you can be confident of the accuracy of any estimates.
The probability of an event is 20 percent How many times should you expect this event to occur in the given number of trials 15 trials 40 trials 75 trials 120 trials?
15 trials: 3 times 40 trials: 8 times 75 trials: 15 times 120 trials: 24 times But don't bet on it.
When do you have a geometric distribution?
A geometric distribution comes from a binary probability which does not have a set number of trials. It seeks to determine how many trials must be conducted before success is achieved. For example, instead of saying, "If I shoot the ball 5 times, what is my probability of success," a geometric probability would question, "How many times will I have to shoot the ball before I make a basket?"
How many times would you expect draw the ace of spades if you have 13 cards in each of four suits and draw one card?
The probability of drawing the Ace of Spades from a standard deck of 52 cards is 1 in 52, or about 0.01923. However, the number of times you can expect to draw it depends on random statistics. If you tested this a large number of times, shuffling the deck each time, you would expect about 1 out of every 52 trials to be the Ace of Spades, but that would only be in the long run, say for thousands and thousands of trials, and even then, it would not be exact. This is the difference between theoretical probability and experimental probability. Theoretical probability is based on pure statistics and the arrangement of the test. All you can say is that, for an infinite number of trials, you would expect 1 out of 52. In the case of experimental probability, you are limited by the number of trials that you can perform. Lets say you ran 10,000 trials. Theoretically, you would expect to draw the Ace of Spades about 192 times. In practice, you would have a range of results.
Why are bacterial toxins are beneficial to medicine?
A beneificial use is that researchers can perform advanced experiments on many different diseases!!!!!!!!!!!!!!!!!!!!
What is an experiment in math?
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.
What is scientific probability?
It is a number expressing the likelihood of the occurrence of a given event, especially a fraction expressing how many times the event will happen in a given number of tests or experiments.
I rolled the dice 120 times what's the probability of getting a 6?
If you rolled a die 120 times, the probability of getting a 6 is one in six. It does not matter how many times you roll the die - the probability is still one in six - except that the long term mean will approach the theoretical value of 0.166... as the number of trials increases.
How do you compute the probability of an event?
There are two main ways: One is to calculate the theoretical probability. You will need to develop a model for the experiment and then use the laws of science and mathematics to determine the probability of the event (subject to the model's assumptions). A major alternative is the empirical or experimental method. This requires performing the trial many times. The probability of the event is estimated by the proportion of the total number of trials which result in the outcome of interest occurring.
