Ch 17 Alternative Hypotheses, P-Values & Using the TI-84 Calculator

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Match each term with the description

Draggable item		Corresponding Item
		What we should conclude is true if the data are out of line with our original assumption about the parameter.
		The value from the population or observed in the past that is claimed not to have changed or to not be different in the null hypothesis.
		We calculate a proportion from the sample, then look to see how likely it would be to observe this statistic if the null hypothesis were actually true.
		What we should do if it's reasonable to believe that our observed data could have occurred by chance, nothing unusual has happened.
		We accept or reject the null hypothesis, give our reason and state this in context of the problem.
		We reject the null hypothesis when in fact it is true. Like a false negative.
		We don't reject the null hypothesis when in fact it was false. Like a false positive.
		Used to determine statistical significance. A calculation that tells us how many standard deviations a value is away from the population proportion or mean.

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We state our conclusion in the context of the original question.
We form a null hypothesis specifying the parameter of a model we'll test using our data.
We proceed with our test, drawing the hypothesized model and calculating the z-value for the data.
We make a decision: if the data are far out of line with the null hypothesis model, we will reject the null hypothesis that nothing has happened, OR if the data are consistent with the null hypothesis model, we will not reject the hypothesis.
We check the appropriate assumptions and conditions.
We form an alternative hypothesis that is either one- or two-tailed based on what we want to learn.

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