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## z tests for percentages
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## z tests for percentages
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This test can be used if:
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This test can be used if:
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- The data is a simple random sample from the population of interest
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- The data is a simple random sample from the population of interest
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- The sample size is large
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- The sample size is large ($>30$)
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- A qualitative variable of interest summarized by percentages
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- A qualitative variable of interest summarized by percentages
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- Can use a box with tickets of 1s and zeros to represent the population
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- Can use a box with tickets of 1s and zeros to represent the population
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If an observed value is too many SEs away from the expected value, it is hard to explain by chance.
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If an observed value is too many SEs away from the expected value, it is hard to explain by chance.
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This test will look very similar to a z test for percentages, it still requires that a large, random, sample was given.
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This test will look very similar to a z test for percentages, it still requires that a large, random, sample was given.
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## t tests for averages
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## t tests for averages
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This test is used when you have a small sample size (lt 30).
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This test is used when you have a small sample size ($<30$).
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The only major differences used with a *t* test is that you use SD+, and
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The only major differences used with a *t* test is that you use SD+.
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With a small sample size, the standard deviation will be relatively higher, so this is compensated with the $SD_+$.
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$$ SD_+ = \sqrt{\frac{size\space sample}{sample\space size}}*SD$$
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This found value is then used in all further calculations where you would normally use the $SD$ in a z score test.
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$$ t = \frac{obs_{ave} - EV_{ave}}{SE_{ave}} $$
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## P Value
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## P Value
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The chance of observing at least a sample statistic, or something more extreme, if the null hypothesis is true.
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The chance of observing at least a sample statistic, or something more extreme, if the null hypothesis is true.
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If the p-value is less than *5*%, reject the null hypothesis.
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If the p-value is less than *5*%, reject the null hypothesis.
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