You improve your model through a better understanding of the underlying processes.
Although more trials will improve the accuracy of experimental probability they will make no difference to the theoretical probability.
When you increase the number of trials of an aleatory experiment, the experimental probability that is based on the number of trials will approach the theoretical probability.
Increasing alpha from .01 to .05 will increase the probability of rejecting the null hypothesis when it is true.
motility
The answer depends on how often you roll it! For one roll it is 1/6 but the probability increases to a near certainty as you increase the number of rolls.
one minute or less
When you increase the number of trials of an aleatory experiment, the experimental probability that is based on the number of trials will approach the theoretical probability.
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.
The probability increases.The probability increases.The probability increases.The probability increases.
Yes.
how did you incresed the accuacy of your catapult
Repetition.
Increasing alpha from .01 to .05 will increase the probability of rejecting the null hypothesis when it is true.
First, it is important to note that it is very unlikely that the experimental and theoretical probabilities will agree exactly. As an extreme example, if you toss a coin an odd number of times, the resulting experimental probability cannot possibly be exactly 1/2. It should be easy to see that this remains true even if the coin is tossed googleplex+1 number of times.A negative difference could be because the number of trials was too small and, with an increased number of trials, the experimental probability would gradually increase towards the theoretical probability.It is also possible that the theoretical model is wrong. You may have assumed that the coin that was being tossed was fair when it was not. Or there were some factors that you failed to take full account of in your theoretical model.Or, of course, it could be a mixture of both.First, it is important to note that it is very unlikely that the experimental and theoretical probabilities will agree exactly. As an extreme example, if you toss a coin an odd number of times, the resulting experimental probability cannot possibly be exactly 1/2. It should be easy to see that this remains true even if the coin is tossed googleplex+1 number of times.A negative difference could be because the number of trials was too small and, with an increased number of trials, the experimental probability would gradually increase towards the theoretical probability.It is also possible that the theoretical model is wrong. You may have assumed that the coin that was being tossed was fair when it was not. Or there were some factors that you failed to take full account of in your theoretical model.Or, of course, it could be a mixture of both.First, it is important to note that it is very unlikely that the experimental and theoretical probabilities will agree exactly. As an extreme example, if you toss a coin an odd number of times, the resulting experimental probability cannot possibly be exactly 1/2. It should be easy to see that this remains true even if the coin is tossed googleplex+1 number of times.A negative difference could be because the number of trials was too small and, with an increased number of trials, the experimental probability would gradually increase towards the theoretical probability.It is also possible that the theoretical model is wrong. You may have assumed that the coin that was being tossed was fair when it was not. Or there were some factors that you failed to take full account of in your theoretical model.Or, of course, it could be a mixture of both.First, it is important to note that it is very unlikely that the experimental and theoretical probabilities will agree exactly. As an extreme example, if you toss a coin an odd number of times, the resulting experimental probability cannot possibly be exactly 1/2. It should be easy to see that this remains true even if the coin is tossed googleplex+1 number of times.A negative difference could be because the number of trials was too small and, with an increased number of trials, the experimental probability would gradually increase towards the theoretical probability.It is also possible that the theoretical model is wrong. You may have assumed that the coin that was being tossed was fair when it was not. Or there were some factors that you failed to take full account of in your theoretical model.Or, of course, it could be a mixture of both.
Likely the best way to increase accuracy in perception is to submit the perception to logic. Trying to understand them in a logical way expands the perception to more than one sense.
Target or Range.
The easiest way to increase your accuracy in distance shooting is to start out closer, and then practice going farther and farther away until you eventually get better.
motility