Why Study Probability?

To begin Statistical Inference, to measure uncertainty ...

Probability (definition): is a measure of likelihood or chance, expressed as a number (or %) between 0 and 1 (or 0% and 100%).

We use information from a sample to make inferences about a population (where info is generally unknown) based on probabilities or likelihoods of events. 

Get familiar with the terminology in statistics!

An experiment is a process that allows us to obtain observations.  Examples of experiments -- flipping a coin, tossing a die, choosing a card, etc.  Ask yourself, how many total possible outcomes?  When flipping a coin, there are 2 outcomes (heads or tails).

An event or outcome is the collection of results of an experiment.  [Note the difference between a simple event (example: a 4 of clubs) vs. a compound event (example: a club).]  How many events are there when tossing a die?  Answer -- 6 events.

A sample space is the set of all possible simple events for an experiment.

                S = {1, 2, 3, 4, 5, 6} when tossing a die, where e1 = 1, e2 = 2, e3 = 3, and so on ...

Then the probability of an outcome, say the probability of rolling a die and getting a 1 is written as P(1) = 1/6 and also consider that P(1) = P(2) = P(3) = P(4) = P(5) + P(6) = 1/6 if all outcomes are equally likely.

Statistics starts out with "Easy Stuff" ...

Measures of central tendency (mean, median, midrange, mode), illustrating frequency distributions (histogram, pie charts, ogive, box plot), and measures of dispersion (range, deviation from mean, variance, standard deviation), as well as ideas such as sample vs. population data, etc. really are very straight-forward concepts and all are very useful tools in statistics. 

Your statistics course is divided into two main sections --- descriptive statistics (see the concepts above) and then inferential statistics.  

Don't just "skim over" the easy, beginning stuff as you will see it all again when you get to the more challenging chapters.