For problems 6 and 7 above one is testing to see if 0.410 is statistically signi
ID: 3129549 • Letter: F
Question
For problems 6 and 7 above one is testing to see if 0.410 is statistically significantly different from 0.400. Both sample sizes were identical (n = 6) and both means were the same (mean = 0.410), yet problems 6 and 7 gave different conclusions or statistical results. Thus to see if two numbers (sample mean and standard) are statistically "equal" to each other, how close or far apart these two numbers are is NOT ALWAYS THAT IMPORTANT. What is important then? How does your answer to a above relate to experimental design and why is it important to keep errors to a minimum in number and magnitude? When you do the above means tests in this homework you are using inferential statistics which means that you are using inductive reasoning and not deductive reasoning. Explain what is meant by inductive reasoning and why biologists must use it instead of deductive reasoning. For problems 9 and 10, use the method of Pearson and Hartley (handout #3). If for a data set: y = 48.6, n = 50 and s = 2.18, then: a.JGive the highest acceptable value for a data point at the 5% significance level. Give the lowest acceptable value for a data point at the 5% significance level. Give the highest acceptable value for a data point at the 1% significance level Give the lowest acceptable value for a data point at the 1% significance level.Explanation / Answer
8.
a)
It is how consistent the differences are, not how big they are. Statistically, this is measured by the P value.
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b)
Hence, we need to minimize errors because we need to know how consistent the differences are. Getting much errors may "blur" our vision when it comes to how consistent the differences are, especially if the differences are just small.
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c)
Inductive reasoning is reasoning based from a sample to generalize a population. Biologists use it often because it is often impractical to include all the population in the experiment.
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