Practice problems for these concepts can be found at:
 Confidence Intervals and Introduction to Inference Multiple Choice Practice Problems for AP Statistics
 Confidence Intervals and Introduction to Inference Free Response Practice Problems for AP Statistics
 Confidence Intervals and Introduction to Inference Review Problems for AP Statistics
 Confidence Intervals and Introduction to Inference Rapid Review for AP Statistics
So far we have used confidence intervals to estimate the value of a population parameter (μ, p, μ_{1} – μ_{2}, p_{1} – p_{2}). In this lesson, we test whether the parameter has a particular value or not. We might ask if p_{1} – p_{2} = 0 or if μ = 3, for example. That is, we will test the hypothesis that, say, p_{1} – p_{2} = 0. In the hypothesistesting procedure, a researcher does not look for evidence to support this hypothesis, but instead looks for evidence against the hypothesis. The process looks like this.
 State the null and alternative hypotheses in the context of the problem. The first hypothesis, the null hypothesis, is the hypothesis we are actually testing. The null hypothesis usually states that there is no bias or that there is no distinction between groups. It is symbolized by H_{0}. An example of a typical null hypothesis would be H_{0}: μ_{1} – μ_{2} = 0 or H_{0}: μ_{1} = μ_{2}. This is the hypothesis that μ_{1} and μ_{2} are the same, or that populations 1 and 2 have the same mean. Note that μ_{1} and μ_{2} must be identified in context (for example, μ_{1} = the true mean score before training).
 Identify which test statistic (so far, that's z or t) you intend to use and show that the conditions for its use are present. We identified the conditions for constructing a confidence interval in the first two sections of this chapter. We will identify the conditions needed to do hypothesis testing in the following chapters. For the most part, they are similar to those you have already studied.
 Compute the value of the test statistic and the Pvalue.
 Using the value of the test statistic and/or the Pvalue, give a conclusion in the context of the problem
The second hypothesis, the alternative hypothesis, is the theory that the researcher wants to confirm by rejecting the null hypothesis. The alternative hypothesis is symbolized by H_{A} or H_{a}. There are three possible forms for the alternative hypothesis: ≠, >, or <. If the null is H_{0}: μ_{1} – μ_{2} = 0, then H_{A} could be:
H_{A}: μ_{1} – μ_{2} ≠ 0 (this is called a twosided alternative
or
H_{A}: μ_{1} – μ_{2} > 0 (this is a onesided alternative)
or
H_{A}: μ_{1} – μ_{2} < 0 (also a onesided alternative).
(In the case of the onesided alternative H_{A}: μ_{1} – μ_{2} > 0, the null hypothesis is sometimes written: H_{0}: μ_{1} – μ_{2} ≤ 0. This actually makes pretty good sense: if the researcher is wrong in a belief that the difference is greater than 0, then any finding less than or equal to 0 fails to provide evidence in favor of the alternative.)
If you are going to state a significance level α it can be done here.

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