BIOL 1004 Lecture Notes - Lecture 9: Genotype Frequency, Prnp, Null Hypothesis

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scarletcheetah369

31 Jan 2017

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Biology lecture monday 30 january 2017 (cid:862)a closer look at genetic drift, gene flow, non-random mating and an introduction to statistics(cid:863) Statistical tests work by falsification: in science we use analysis to see if a statement, called a null hypothesis, can be falsified with the available data. The null hypothesis posits that there is no effect of one variable on another which the alternative hypothesis posits that there is indeed an effect of one variable over the other variable. In some instances, the alternative hypothesis can direction in one way or another (ie. height differences between men and women) or it can just say whether or not there is a difference at all. In order to accept the alternative hypothesis you must show with a reasonable level of confidence that the null hypothesis is incorrect. In biology, the accepted level of confidence is 0. 05 (5%).

Related Questions

4 According to a commercial, drinking water that has been blessed by Bill Nye the Science Guy improves your test scores. You think this is complete nonsense and want to disprove it. So you set up an experiment.

4a Would this be an observational or an empirical study?

4b To test it, for a total of 10 weeks, you take one test every week. Alternating weeks, you will drink regular water (control) and blessed water (treatment). How many replicas will you have?

4c What would be your null-hypothesis?

4d What would be your alternative hypothesis?

4e Name 3 possible confounding factors.

4f What is your independent variable?

4g Wat is your dependent variable?

4hs You set your a-level as always at 0.05, and do a statistical analysis to see if there is a difference in test scores between the two groups. The statistical analysis result is p=0.06 Is there a significant difference in test scores? Yes / No / Donât know

4i Based on the statistical analysis outcomes, does drinking blessed water improve test results? Yes / No / Donât know

4j 10 points A placebo effect is an improvement in a personâs perceived health from taking a non-active ingredient, presumably because of the belief that they are receiving a treatment helps that person to feel better. Your mind is a powerful thing and certainly can influence your health in real ways, so a placebo effect would include real improvements that come from âmind over matterâ as well as simple changes in perception. How can you avoid the placebo effect in this study?

ivoryraven299

How does the biology community currently feel with regards topublishing descriptive and effect size statistics rather thansignificance stats? Almost every journal article I read in the cellbiology field almost always reports things like P values and statstests to report statistical significance, but should effect sizesbe more important to a biologist? Do we even care if something isstatistically significant if the effect size is negligible? Ratherthan crunch for significance, could one get away with showingthings like confidence intervals, eta^2, Cohen's d, and r valuesinstead over P values? P values tell you the odds that if youassume the null hypothesis is true, then the observation you'remaking are only 5%(assuming of course P<0.05). However, this canlead to the logical fallacy as noted by Aristotle--theory Apredicts that changing X will cause Y. An experimenter thusperforms experiments to manipulate X and sees changes in Y,therefore he/she concludes theory A is supported, which is howevercompletely wrong. Theories B, C, D, E....... could all also predictthat X changes Y and may even be better at it. Even if you concludethat your findings "support" theory A, it's still weak because youhaven't ruled out all of the other possibilities.

So in order to avoid statistical significance relative to nullhypothesis that has all sorts of pitfalls, can one just usedescriptive and effect size statistics just as effectively, if notmore so?

BIOL 1004 Lecture Notes - Lecture 9: Genotype Frequency, Prnp, Null Hypothesis

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