It takes an average of 10.2 minutes for blood to begin clotting after an injury. An EMT wants to see if the average will decline if the patient is immediately told the truth about the injury. The EMT randomly selected 60 injured patients to immediately tell the truth about the injury and noticed that they averaged 10.1 minutes for their blood to begin clotting after their injury. Their standard deviation was 1.62 minutes. What can be concluded at the the a = 0.01 level of significance? a. For this study, we should use t-test for a population mean b. The null and altenative hypotheses would be: Hạ: P 10.2 10.2 c. The test statistic tv = -0.478 (please show your answer to 3 decimal places.) d. The p-value = 0.3172 (Please show your answer to 4 decimal places.) e. The p-value is >va f. Based on this, we should reject | the null hypothesis. g. Thus, the final conclusion is that .. O The data suggest the population mean is not significantly less than 10.2 at a = 0.01, so there is statistically significant evidence to conclude that the population mean time for blood to begin clotting after an injury if the patient is told the truth immediately is equal to 10.2. O The data suggest that the population mean is not significantly less than 10.2 at a = 0.01, so there is statistically insignificant evidence to conclude that the population mean time for blood to begin clotting after an injury if the patient is told the truth immediately is less than 10.2. O The data suggest the populaton mean is significantly less than 10.2 at a = 0.01, so there is statistically significant evidence to conclude that the population mean time for blood to begin clotting after an injury if the patient is told the truth immediately is less than 10.2.

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Continuous Probability Distributions

Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.

Normal Distribution

Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!