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Friday, Sept. 5

Cal. Friday, Sept. 5. Objectives. Tonight’s HW. Type your bean data into an Excel spreadsheet and save on a flashdrive. State the purpose of a T test. Notes. 6 people per lab group. Fork Subgroup. Spoon Subgroup. 1. No-look scooper 2. Counter and bean-put-backer 3. Recorder.

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Friday, Sept. 5

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  1. Cal. Friday, Sept. 5 Objectives Tonight’s HW Type your bean data into an Excel spreadsheet and save on a flashdrive • State the purpose of a T test Notes

  2. 6 people per lab group Fork Subgroup Spoon Subgroup 1. No-look scooper 2. Counter and bean-put-backer 3. Recorder • 1. No-look scooper • 2. Counter and bean-put-backer • 3. Recorder Cross off trials 26-50 We are only doing 25 trials for each utensil Recorders will share the data with the 6 person group at the end so everyone has all of the data

  3. Bean Lab Data • This weekend, download the Bean Lab Data Template from the class website. • www.hausclass.mccaskey.wikispaces.net • IB605 Period 8 • Assignments • Type in your data • Save onto your flashdrive • NameBeanLabData

  4. Notes The T Test

  5. Our results show a very small overlap between the two sets of data. So how do we know if the difference is significant or not? We need to use a statistical test. Thet-test is a statistical test thathelps us determine the significance of the differencebetween the means of two sets of data.

  6. P Values • Most of the time, we will be using a p value of p= 0.05 • This means the probability of the difference being due to random chance is only 5% • Another way to look at it is that we are 95% certain that the results are different enough to draw the conclusion that the results are significantly different

  7. P values vs. T values huh? • The t value will always be calculated for you. It combines data from the mean, standard deviation, and sample size • The P value reflects the confidence we have in the significance of our results

  8. We compare the T value to critical values that depend on the size of our sample and the level of confidence we need. “Degrees of Freedom (df)” is the total sample size minus two. What happens to the critical value as the confidence level increases? Example two-tailed t-table. “critical values”

  9. We can calculate the value of ‘t’ for a given set of data and compare it to critical values that depend on the size of our sample and the level of confidence we need. Example two-tailed t-table. If the t value is higher than the critical value in the table, your results are significantly different and you reject your null hypothesis If t is higher than the critical value in the table, your results are not significantly significant and you accept the null hypothesis “critical values”

  10. Example problems • Let’s Work through the example on page 6 in the chapter 1 text together • Then, let’s do 6d from the same packet in the back

  11. Excel can jump straight to a value of P for our results. One function (=ttest) compares both sets of data. As it calculates P directly (the probability that the difference is due to chance), we can determine significance directly. In this case, P=0.00051 This is much smaller than 0.005, so we are confident that we can: reject H0. The difference is unlikely to be due to chance. Conclusion: There is a significant difference in bill length between A. colubris and C. latirostris.

  12. 95% Confidence Intervals can also be plotted as error bars. no overlap =CONFIDENCE.NORM(0.05,stdev,samplesize) e.g=CONFIDENCE.NORM(0.05,C15,10) • These give a clearer indication of the significance of a result: • Where there is overlap, there is not a significant difference • Where there is no overlap, there is a significant difference. • If the overlap (or difference) is small, a t-test should still be carried out.

  13. Interesting Study: Do “Better” Lecturers Cause More Learning? Students watched a one-minute video of a lecture. In one video, the lecturer was fluent and engaging. In the other video, the lecturer was less fluent. They predicted how much they would learn on the topic (genetics) and this was compared to their actual score. (Error bars = standard deviation). Find out more here: http://priceonomics.com/is-this-why-ted-talks-seem-so-convincing/

  14. Interesting Study: Do “Better” Lecturers Cause More Learning? Students watched a one-minute video of a lecture. In one video, the lecturer was fluent and engaging. In the other video, the lecturer was less fluent. They predicted how much they would learn on the topic (genetics) and this was compared to their actual score. (Error bars = standard deviation). Is there a significant difference in the actual learning? Find out more here: http://priceonomics.com/is-this-why-ted-talks-seem-so-convincing/

  15. Dog fleas jump higher that cat fleas, winner of the IgNobel prize for Biology, 2008. http://www.youtube.com/watch?v=fJEZg4QN760

  16. Correlation does not imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing "look over there." Cartoon from: http://www.xkcd.com/552/

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