Consider the fact though that pulling one sample from a population could produce a statistic that isn’t a good estimator of the corresponding population parameter. To correct for this, instead of taking just one sample from the population, we’ll take lots and lots of samples, and create a sampling distribution of the sample mean.

Read MoreA **discrete random variable** is a variable that can only take on discrete values. For example, if you flip a coin twice, you can only get heads zero times, one time, or two times; you can’t get heads 1.5 times, or 0.31 times.

With any hypothesis test, we need to state the null and alternative hypotheses, then determine the level of significance. We’ve already covered these first two steps, and now we want to learn how to calculate the test statistic, which will depend on whether we’re running a two-tail test or a one-tail test.

Read MoreIn this lesson, we want to see what happens to our measures of central tendency and spread when we make changes to our data set. Specifically the changes made either by changing all the values in the set at once, or by adding a single data point to, or removing a single data point from, the data set.

Read MoreBar graphs and pie charts can both be used to represent the same set of data. Pie charts will work better for some data sets, but bar graphs will work better for others.

Read MoreSometimes we talk about two-way data in terms of **independent variables** and **dependent variables**. In the case of one-way data, we had one independent variable, called the individuals, and one or more dependent variables, called the variables. In the case of two-way data, we have two independent categories on which the variables are dependent.

A **relative frequency histogram** is the same as a regular histogram, except that we display the frequency of each category as a percentage of the total of the data. A frequency polygon is a polygon-shaped figure that shows the frequency at which each category occurs in the data set.

A Bernoulli random variable is a special category of binomial random variables. Specifically, with a Bernoulli random variable, we have exactly one trial only (binomial random variables can have multiple trials), and we define “success” as a 1 and “failure” as a 0.

Read MoreType I error rate is the rejecting the null hypothesis when it’s true, and Type II error rate is the probability of accepting the null hypothesis when it’s false. Type I error is called “alpha,” and Type II error is called “beta.”

Read MoreIt’s important to know whether we’re talking about a population or a sample, because in this section we’ll be talking about variance and standard deviation, and we’ll use different formulas for variance and standard deviation depending on whether we’re using data from a population or data from a sample.

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