hyp_test_answer_keys.pdf

# 0001 chance of getting the sample data or more

• Test Prep
• 29

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If Ho is true, then there was less than 0.0001 chance of getting the sample data or more extreme. Reject Ho There is significant evidence to support that there is a higher percent of females in math 140 than males. 10. P1: percent of Instagram P2: percent of Facebook 1 2 1 2 : p p : p < p Ho Ha claim Assumption: Not random, but was a census. At least 10 people that use Instagram and at least 10 that do not use Instagram. At least 10 that use facebook and at least 10 that do not use facebook. Test Stat: 4.16 (Instagram was 4.16 standard errors above facebook) (Significantly higher NOT lower!!) Pvalue = 1 If the null is true, then there is a 100% chance of getting the sample data or more extreme. Fail to reject Ho. There is not significant evident to support that percent of Instagram is lower than the percent of facebook.

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Hypothesis Test Review Sheet #1 Answers 1. Simulation is an important part of inferential statistics. The idea is to assume that the population value in the null hypothesis is correct and simulate what we would expect random samples from that population value to look like. Now look at a real sample data set and compare it to the simulation. We can look at how many of the simulated sample values were the same or more extreme as the real sample data value. The probability of this is an approximate P-value. If the real sample data value happened a lot in the simulation, then we are pretty sure that the real sample value could have happened by random chance. Therefore the real sample value is not significantly different than the population value that the simulation is based on. 2. A test statistic tells us how many standard errors the sample data is above or below the population value used in the null hypothesis. 3. There are several ways to know that the sample data is significantly different. A test statistic that is very unusual or a very small P-value can indicate this. Also a value that is very rare in the simulation can also. 4. If the population value in the null hypothesis is correct, then the P-value is the probability of getting the sample value or more extreme. 5. There are several ways to know if the sample data could have happened by random chance and does not necessarily contradict the population value. A test statistic that is very small or a very large P-value can indicate this. Also a value that is happens often in the simulation can also. 6. If P-value is small (less than or equal to the significance level), reject the null hypothesis. If P-value is large (greater than the significance level), fail to reject the null hypothesis. 7. If you reject the null hypothesis, start conclusion with “there is significant sample evidence”. If you fail to rejec t the null hypothesis, start conclusion with “there is not significant sample evidence”.
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• Fall '17
• Statistics, Statistical hypothesis testing, significant sample evidence

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