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 theoretical probability provides a model for predicting the outcome of trials. You then carry out a number of trials. Compare the outcome of your trials with the results predicted by the theoretical model. The comparison will usually involve "hypothesis testing", a branch of statistics. This is a method to test how likely the actual outcomes are if the theoretical probabilities were true. The exact nature of the test will depend on the theoretical basis and so the answer cannot be simplified.
No. The more trials the better. You can only estimate the probability of an outcome based on the data from experimentation. But if you find that the percentage in 90 trials is practically identical to the percentage in 30 trials, that is an indication that the percentage will hold true for even larger numbers of trials.
Number of trials is how many times you test your hypothesis. When you are doing trials the end result may come out differently every time.
The relative frequency of an event, from repeated trials, is the number of times the event occurs as a proportion of the total number of trials - provided that the trials are independent.
The probability that is based on repeated trials of an experiment is called empirical or experimental probability. It is calculated by dividing the number of favorable outcomes by the total number of trials conducted. As more trials are performed, the empirical probability tends to converge to the theoretical probability.
The few small and relatively short clinical trials of pygeum in the treatment of Hepatitis C and HIV+ infections have been statistically significant; further trials are under way in South Africa.
If the results get ridiculously high or low/or if they needed to do more trials/or if they missed some procedures
There is no set number of trials considered universally acceptable in an experiment. The number of trials needed can vary depending on the nature of the experiment, the desired level of statistical significance, and other factors. Typically, researchers aim for a sufficient number of trials to ensure reliable results.
The number of trials and sample sizes generally increase the accuracy of the results because you can take the average or most common results in the experiment
Trials or experiments.
Scientists do multiple trials and find the mean of the trials to make their results reliable-this eliminates the impact any anomalies may have.
repeated trials
So that you could compare results
To ensure an experiment's results are valid, you must conduct multiple trials to account for variability and increase reliability. This helps to minimize potential errors and ensure that the results are consistent and reproducible.
To calculate the average for multiple trials in a chemistry experiment, add up the results of all the trials and then divide by the number of trials conducted. This will give you an overall average value that represents the combined results of all the trials. Averaging helps to minimize the impact of outliers and provides a more reliable estimate of the true value.
to make your results more reliable
The Nuremberg trials were significant because Nuremberg was the city in Germany where the Nuremberg Laws were created, which deprived Jews of German citizenship. The trials were held in Nuremberg because it was almost like a punishment for the Nazis.