1.In order to find the experimental probability that a particular result will occur, you have to do a bunch of trials and take note of the outcomes. In short, meaning you have to actually do the event. There are no calculations involved, but this way you have the knowledge of what the result's probability is going to be. It is called the experimental probability because you are the one experimenting the event and then taking note of what the outcome is. And then using that information to see what the likeliness or probability of that happening is.
For example, Jenna is finding the experimental probability of getting red when she flips a counter. She does 50 trials. She records the outcome whenever she flips the counter to see whether it lands on red or yellow. The results end up being 32/50 for yellow and 18/50 for red. That concludes that in her experiment, 36% of the time the counter landed on red.
2. No, 30 trials is not as good as 500 trials in an experiment. The reason why is the more trials you do, the more accurate the outcome is. If you do more trials, you are able to have an outcome, that is more likely to happen in the event.
3. When a result is equally likely, it means that the result could go one way or another, but the chances are the same. That means that it is fair, meaning that it's not likely for one outcome to be more likely than the other.
For example, when you flip a coin and get heads or tails. At the first try you have a fair chance of getting either heads or tails because you only have two options. So if you put that in a percent, you get 50/50 chance for getting either heads or tails.
Also, if you have for example a deck of cards. There are 52 cards in a deck and there are 13 of each suit, meaning the spades, hearts, diamonds, and clubs. The chance of getting a heart is 25% because 13/52 simplified is 1/4, which in a percent is 25%. This means you have 25% chance of getting either spades, hearts, clubs, or diamonds, which means it is fair. It would be unfair if the dealer or person handling everything added a heart, that would make the chances of getting a heart more likely than getting a spade, club, or diamond.
For example, Jenna is finding the experimental probability of getting red when she flips a counter. She does 50 trials. She records the outcome whenever she flips the counter to see whether it lands on red or yellow. The results end up being 32/50 for yellow and 18/50 for red. That concludes that in her experiment, 36% of the time the counter landed on red.
2. No, 30 trials is not as good as 500 trials in an experiment. The reason why is the more trials you do, the more accurate the outcome is. If you do more trials, you are able to have an outcome, that is more likely to happen in the event.
3. When a result is equally likely, it means that the result could go one way or another, but the chances are the same. That means that it is fair, meaning that it's not likely for one outcome to be more likely than the other.
For example, when you flip a coin and get heads or tails. At the first try you have a fair chance of getting either heads or tails because you only have two options. So if you put that in a percent, you get 50/50 chance for getting either heads or tails.
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Qu's 2 - correct but general, (exactly how to do things and how will they change?), clearer examples/counter examples possible
ReplyDeleteQu 1 & 3 good use of examples
Be careful with exact conclusions like "no calculations" - yes there still is, a simple one
9/10 + 4/5 = 13 = A