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17 Cards in this Set
- Front
- Back
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What is a signficance test?
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A significance test is a way of statistically gesting a hypothesis by comparing the observed data to the predicted values.
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What are the five elements that all statistical tests have?
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1. assumptions
2. hypotheses 3. test statistic 4. P-value 5. conclusion |
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All statistical tests require certain assumptions for the test to be valid. What are the 4 assumptions?
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1. type of data (qual. or quan)
2. pop. distribution (normal?) 3. method of sampling (simple random, for instance) 4. sample size. |
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What are the type types of hypotheses that a significance test considers about the value of a parameter?
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1. Null hypothesis (no effect)
2. Alternative hypothesis (some effect) |
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What is another name given to the alternative hypothesis?
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- The research hypothesis.
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What is the test statistic?
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The test statistic is the statistic calculated from the sample data to test the null hypothesis. It usually refers to a point estimate of the parameter.
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What is the P-Value
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The P-Value is the probability that, if the null hypothesis were true, that the test statistic would fall in this collection of values.
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If one had a very small P-value what could be inferred about the null hypothesis?
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A small P-value indicates that the data contradicts the null hypothesis (it can be rejected). Large P-values support the null.
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What P-value do most studies require in order to reject the null?
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Most require a P-value of P=.05
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What would a P-Value of .83 indicate? How about .0002
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.83 - keep null hypothesis
.0002 - reject null! |
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What is the alpha-lvl?
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The alpha lvl is a number determined before testing that says at what probability lvl the null hypothesis is rejected.
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What is the alpha lvl also called?
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The alpha lvl is also called the significance lvl of a test
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What are the most common alpha levels?
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Alpha = 0.05
Alpha = 0.01 Remember - the smaller the alpha lvl, the stronger the evidence must be to reject the null. |
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Do A&F think it is better to:
a) report the P-value; or b) say that a result is statistically significant at a certain alpha lvl. |
A & F think it is better to report the p-value because they argue it is artificial to simply call one result significant and the other not.
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Explain what Type I and Type II errors are.
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Type I - when null is rejected even though it is true;
Type II - when null is not rejected even though it is false. |
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How are Type I and Type II errors related, if at all?
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Type I and Type II errors are inversely related. As one decreases the other increases.
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When might the probability of committing a Type II error be rather large?
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If the sample size is small there is a greater chance that one would commit a Type Ii error (ie: the null is not rejected though it is false)
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