14. Describe the difference between classical and empirical probability. Choose the correct answer below. A. The classical method obtains an exact probability of an event by conducting a probability experiment. The empirical method of computing empirical probabilities does not require that a probability experiment actually be performed. Rather, it relies on counting techniques, and requires equally likely outcomes. B. The classical method obtains an approximate empirical probability of an event by conducting a probability experiment. The empirical method of computing probabilities does not require that a probability experiment actually be performed. Rather, it relies on counting techniques, and requires equally likely outcomes. C. The empirical method obtains an exact empirical probability of an event by conducting a probability experiment. The classical method of computing probabilities does not require that a probability experiment actually be performed. Rather, it relies on counting techniques, and requires equally likely outcomes. D. The empirical method obtains an approximate empirical probability of an event by conducting a probability experiment. The classical method of computing probabilities does not require that a probability experiment actually be performed. Rather, it relies on counting techniques, and requires equally likely outcomes.
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Bonus Knowledge
The classical probability is like the ideal world of dice and coins, where outcomes are equally likely, and you can calculate the chances with mathematics alone. Imagine flipping a fair coin: the probability of heads or tails is a neat 50% because you know all possible outcomes upfront! On the other hand, empirical probability is all about collecting real data from experiments, making it a bit more grounded. Picture pulling a handful of M&Ms from a bag and observing the colors; that’s empirical probability at work! If you’re ever working with empirical probability, remember: your sample size matters! A small batch might lead to wildly inaccurate estimates. The classic mistake here is assuming that a few trials give you enough data to generalize. So, when counting outcomes in your experiments, aim for a larger sample size to get a reliable probability estimate!
