Statistics discussion board 2
1) What is the difference between probability distributions and frequency distributions? Provide an example that demonstrates the difference between the two.
A probability distribution directly corresponds to a frequency distribution, except that it is based on theory (probability theory), rather than on what is observed in the real world (empirical data). A frequency distribution is based on actual observations. An example would be observing a coin be flipped twenty times. A probability distribution is theoretical or ideal; it portrays what the percentages should be in a perfect world. An example of a probability distribution would be flipping a single coin. There are two possible outcomes (heads or tails) each having a ½= .50 probability.
2) How does a normal curve relate to a probability distribution? How and why can we make predictions about the occurrence of an event using a theoretical normal curve?
The normal curve is a probability distribution and a theoretical ideal. The theoretical model known as the normal curve is a very useful probability distribution for its applications to actual research situations. The symmetrical, bell shaped, normal curve can be used to interpret the standard deviation, to describe distributions of scores, and making statements of probability. In relation to standard scores, we can determine the percent of the total area under the normal curve associated with any given sigma distance from the mean and the probability of obtaining any raw score in a distribution.
3) What is the standard error of the mean and why is it important?
If you take many random samples from a population, the standard error of the mean is the standard deviation of the different sample means. Standard error of the mean is important because the standard error of the mean tells you how accurate your estimate of the mean is likely to be. As you increase your sample size, the standard error of the mean will become smaller. With the help of the standard
A probability distribution directly corresponds to a frequency distribution, except that it is based on theory (probability theory), rather than on what is observed in the real world (empirical data). A frequency distribution is based on actual observations. An example would be observing a coin be flipped twenty times. A probability distribution is theoretical or ideal; it portrays what the percentages should be in a perfect world. An example of a probability distribution would be flipping a single coin. There are two possible outcomes (heads or tails) each having a ½= .50 probability.
2) How does a normal curve relate to a probability distribution? How and why can we make predictions about the occurrence of an event using a theoretical normal curve?
The normal curve is a probability distribution and a theoretical ideal. The theoretical model known as the normal curve is a very useful probability distribution for its applications to actual research situations. The symmetrical, bell shaped, normal curve can be used to interpret the standard deviation, to describe distributions of scores, and making statements of probability. In relation to standard scores, we can determine the percent of the total area under the normal curve associated with any given sigma distance from the mean and the probability of obtaining any raw score in a distribution.
3) What is the standard error of the mean and why is it important?
If you take many random samples from a population, the standard error of the mean is the standard deviation of the different sample means. Standard error of the mean is important because the standard error of the mean tells you how accurate your estimate of the mean is likely to be. As you increase your sample size, the standard error of the mean will become smaller. With the help of the standard