Welcome back!

1. Welcome back!

2. What will you be doing on May 10th, 2014? • http://www.youtube.com/watch?v=Jey_fUDh3vI

3. Official IB Schedule • IA SUBMISSION: MARCH 28, 2014 • IB BIOLOGY FINAL EXAM: MAY 12 (MONDAY) 33 weeks from beginning of term

4. TOPICS FOR FALL TERM STANDARD AND HIGHER LEVEL HIGHER LEVEL ONLY Further Genetics ( 6h) Further Respiration (7h) Further Photosynthesis (5h) Further Ecology (5h) • Statistics (2h) • Genetics (15h) • Respiration (2h) • Photosynthesis (3h) • Further Ecology (6h) • (SL only – Topic A: Human Nutrition and Health) (2h)

5. TOPICS FOR SPRING TERM STANDARD AND HIGHER LEVEL HIGHER LEVEL ONLY Plant science ( 11 h) HL Human physiology ( 17 h) Topic H: further human physiology (15h) • (SL only – Topic A: Human Nutrition and Health) (2h) • Human physiology (9 h)

6. Remaining IA assignments • Genetics IA( DCP and CE) • Photosynthesis IA (Design) • Plant Science IA (Design,DCP and CE) • Human Health and Nutrition (Design, DCP and IA) IA SUBMISSION: MARCH 28, 2014

7. Statistics • How can we know that scientific information is reliable and valid? • Why does Biology need statistical methods?

8. Big questions in Science… What do I need to know about statistics to succeed in IB Biology?

9. Statisticians… ‘..people who like figures, but don’t have the personality skills to become accountants…’ • do uncertainty, randomness and chance have a place in science? • How should we react to them?...

11. What do we need to know about statistics? • ‘Average’: mean, median, mode • ‘Error bars’: Variance, standard deviation, standard error of the mean, (interquartile range) • Significance and probability • T-tests ( 1- and 2- tailed, paired and independent) • Chi-Squared test (genetics IA) • The relationship of causationand correlation • Classic graphs

12. Can statistics help us? • Chocolate gives you spots • Late nights sap young people’s brain power • Coffee can make you see dead people • Mobile phones cause cancer!

13. How do we make sense of data? Look for patterns and outliers in different groups Descriptive statistics Graphs, tables, means and variance You can’t use the results to generalise about the population beyond the data Apply tests to see if the differences we see are of predictive value (reliable): Inferential statistics T-tests Chi-squared tests ANOVA Regression analysis allow us to make inferences(generalisations) about the population beyond our data (based on probability)

14. What do we do with Biological data? • Measure ‘central value’: mean, median, mode • Measure ‘spread’ (variance): range, standard deviation, interquartile range • Compare data sets • Look for relationships (often called correlations) between data sets

15. Inferential statistics use probability (p) values • The p value tells us the likelihood that the difference we observed is real and repeatable • Specifically, the p value is the probability that the difference observed was produced by random data (chance) • If p = 0.10, there is a 10% chance • If p = 0.05, there is a 5% chance • If p = 0.01. there is a 1% chance Scientists accept p < 0.05 as ‘significantly different’

16. Sample size matters Bigger samples make it easier to detect differences A good guideline is to aim for 20 – 30 data points in each test group

17. Looking at data

18. Biological data are often normally distributed • Height • Blood pressure • Heart rate • Marks on an exam • Errors in machine-made products

19. If NOT normally distibuted, data can be skewed (or just jumbled!)

20. An example • Researchers have developed a new drug (tetesterol)to lower serum cholesterol levels • They treat 2 groups for a month with either tetesterol or placebo • After that month, the researchers measure cholesterol in both groups

21. Cholesterol concentration after 1 month… (i.e., does the drug really make a difference?)

22. First, ‘eyeball’ the data: ‘Descriptive statistics’

23. Measure the central tendency (mean, median, mode)

24. Is this difference reliable? (i.e., does the drug really make a difference?) Cholesterol concentration after 1 month

25. Why not just look at the means? The means may show you a difference, but we can’t be sure that it’s a reliable difference Which of these data sets shows the greatest variation?

26. In order to compare test samples, we also need to look at the spread of results

27. Measurement of ‘spread’ (variance): • Range • Variance • Standard deviation • (standard error) • (interquartile range)

28. Range – and its limitations

29. Standard deviation σ • A measure of spread • It is, simply, the square root of the variance • It gives us an idea of the spread of most of the data and is much more reliable than range (less affected by anomalous data) • You just need to press a button • You don’t need to know the formula • (There are links on the Blog if you WANT to know the formula…)

30. Variance Officially: • Variance: the average of the squared differences from the mean in a sample • You calculate it using a calculator or EXCEL

31. Standard deviation • Only applicable to normal distributions • 68% of values are within 1 standard deviation of the mean • 95% of values are within 2 SD’s of the mean

32. Error bars on graphs They are graphical representations of the spread of the data May represent: • Range • Standard deviation • Standard error • Confidence intervals • Interquartile range

33. There are various types of error bar

34. Question check: • Which data set has the highest mean? • Which data set has the highest variance? • What do the error bars represent?

36. Question check:

37. Comparing data

38. Drug trial data

39. Large overlap: lots of shared data… Results are not likely to be significantly different (more likely due to chance) Small or no overlap: very little shared data… Results are likelyto be significantly different (‘real’)

40. Question check:

41. InferentialStatisticsComparing two data sets: The T-test… • Used to compare two normally distributed data sets (ideally with similar variances) • A t-test is a statistic that checks if the means of 2 groups are reliably different • Just looking at the means may show you that they are different, but doesn’t show if the difference is reliable • We always test the NULL Hypothesis (H0) • T-test…the movie…

42. Two main types of T-test Independent (unpaired) samples (most common) E.g. testing the quality of two types of fruit smoothie… Dependent (paired) samples • One group measured at 2 different times • E.g. heart rate before and after exercise

43. So what is the T-value? It’s just a number!

44. Reading, writing and understanding T-tests • (99) = degrees of freedom • How many samples were there in this case? • p= probability of results happening by chance • Are these results significant? • M = mean values

45. So what are degrees of freedom? Degrees of freedom represent sample size. For only one group, df = n-1, wheren = number of samples Usually we are looking at 2 groups, so df = (n1 + n2) -2

46. Question check:

49. Let’s try some…examples from the worksheet 6. In a t-test comparing Group A and Group B, the P value was calculated as 0.004. What does this P value tell us about these two sets of data? Explain your answer. 8. (b.) A student measures 15 snail shells on the north side of an island and 16 on the south. H0= Confidence = DF = Critical value = t is calculated as 2.02. So we reject/accept Ho. Conclusion:

50. Correlations and coincidences