For a review of concepts, see Permutations and Combinations. The study of probability helps us figure out the likelihood of something happening. For instance, when you roll a pair of dice, you might ask how likely you are to roll a seven.
Generalizing to a Population: You are supposed to do it by generating a p value from a test statistic. So let's find out what this p is, what's special about 0. I'll also deal with the related topics of one-tailed vs two-tailed tests, and hypothesis testing.
What is a P Value? It's difficult, this one. P is short for probability: And what's this got to do with statistical significance? I've already defined statistical significance in terms of confidence intervals. The other approach to statistical significance--the one that involves p values--is a bit convoluted.
First you assume there is no effect in the population. Then you see if the value you get for the effect in your sample is the sort of value you would expect for no effect in the population.
If the value you get is unlikely for no effect, you conclude there is an effect, and you say the result is "statistically significant". Let's take an example. You are interested in the correlation between two things, say height and weight, and you have a sample of 20 subjects.
OK, assume there is no correlation in the population.
Now, what are some unlikely values for a correlation with a sample of 20? It depends on what we mean by "unlikely". In that case, with 20 subjects, all correlations more positive than 0. What did you get in your sample?
OK, that's not an unlikely value, so the result is not statistically significant. Or if you got But wait a minute.
What about the p value? That's the way it used to be done before computers. You looked up a table of threshold values for correlations or for some other statistic to see whether your value was more or less than the threshold value, for your sample size. Stats programs could do it that way, but they don't.
That's the p value. A bit of thought will satisfy you that if the p value is less than 0. For an observed correlation of 0. The correlation is therefore not statistically significant. Here's our example summarized in a diagram: The curve shows the probability of getting a particular value of the correlation in a sample of 20, when the correlation in the population is zero.
For a particular observed value, say 0.Using and Handling Data. Data Index.
Probability and Statistics Index. Virginia Department of Education iii Introduction The revised Probability and Statistics for Elementary and Middle School Teachers is a staff development training program designed to assist teachers in implementing the Virginia Standards of Learning for mathematics.
This. Introduction—Uses of Probability and Statistics 9 statistics, I suggest that you merely glance over this Introduc-tion and then proceed directly with Chapter 1. For a review of concepts, see Permutations and Combinations.. The study of probability helps us figure out the likelihood of something happening.
For instance, when you roll a pair of dice, you might ask how likely you are to roll a seven. In probability theory and statistics, the Poisson distribution (French pronunciation: ; in English often rendered / ˈ p w ɑː s ɒ n /), named after French mathematician Siméon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant rate and.
Video: How to Calculate the Probability of Combinations To calculate the probability of a combination, you will need to consider the number of favorable outcomes over the number of total outcomes.