1. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.1 BIO 4118 Applied Biostatistics Scott Findlay Vanier 306, 313, 314 sfindlay@science.uottawa.ca 562-5800 x4574 Scott Findlay Vanier 306, 313, 314 sfindlay@science.uottawa.ca 562-5800 x4574

2. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.2 About me l Not a statistician l So, emphasis will be on practical knowledge and application of statistics, rather than theorems and proofs. l Not a statistician l So, emphasis will be on practical knowledge and application of statistics, rather than theorems and proofs.

3. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.3 Why you should be here l you have an interest in statistical reasoning l you have a desire to learn to use statistics properly in experimental design and data analysis l you want to develop your ability to critically assess scientific (or pseudo-scientific) arguments l you have an interest in statistical reasoning l you have a desire to learn to use statistics properly in experimental design and data analysis l you want to develop your ability to critically assess scientific (or pseudo-scientific) arguments

4. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.4 What is expected of you l attendance at most lectures l attendance at most laboratory sessions l feedback to me on what you like and dislike about the course, especially how it can be improved l attendance at most lectures l attendance at most laboratory sessions l feedback to me on what you like and dislike about the course, especially how it can be improved

5. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.5 ObjectivesObjectives l Understand the fundamental principles of statistical inference. l Understand the general principles underlying the most common tests. l Know the assumptions of common tests and understand impact of violations. l Be able to perform standard statistical analyses with SYSTAT. l Understand the fundamental principles of statistical inference. l Understand the general principles underlying the most common tests. l Know the assumptions of common tests and understand impact of violations. l Be able to perform standard statistical analyses with SYSTAT.

6. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.6 EvaluationEvaluation l 60% Problem assignments l 40% Term projects l 60% Problem assignments l 40% Term projects l All assignments are “open-book”, and can be done in groups of three people or fewer. l Remember, plagiarism is not acceptable !

7. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.7 l Morin, A. & C.S. Findlay 2001. Course Notes for BIO 4118 Applied Biostatistics. University of Ottawa, Ottawa. l Findlay, C.S. & A. Morin 2001. Lecture Presentations for BIO 4118 Applied Biostatistics, Vol. 1. University of Ottawa, Ottawa. l Sokal, R.L. & F.J. Rohlf. 1995. Biometry (3 rd edition), W.H. Freman & Co., New York, or l Zar, J.H. 2000. Biostatistical Analysis (4 th edition), Prentice-Hall, Upper saddle River, New Jersey. l Morin, A. & C.S. Findlay 2001. Course Notes for BIO 4118 Applied Biostatistics. University of Ottawa, Ottawa. l Findlay, C.S. & A. Morin 2001. Lecture Presentations for BIO 4118 Applied Biostatistics, Vol. 1. University of Ottawa, Ottawa. l Sokal, R.L. & F.J. Rohlf. 1995. Biometry (3 rd edition), W.H. Freman & Co., New York, or l Zar, J.H. 2000. Biostatistical Analysis (4 th edition), Prentice-Hall, Upper saddle River, New Jersey. TextsTexts

8. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.8 Class preparation l Read appropriate chapter(s) in lecture notes beforehand and bring questions to class. l If you’ve got a question, ask it immediately! There is no such thing as a stupid question! l For labs, read appropriate section(s) in lecture notes beforehand. l Read appropriate chapter(s) in lecture notes beforehand and bring questions to class. l If you’ve got a question, ask it immediately! There is no such thing as a stupid question! l For labs, read appropriate section(s) in lecture notes beforehand.

9. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.9 Extended classroom structure Lectures Laboratory Course Web Me

10. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.10 Accessing the course web l Obtain account for one of campus servers (e.g. for Proktor (Faculty of Science)). l Log on using your user ID and password, then launch Netscape. l Enter www.edteched.uottawa.ca in the location field.www.edteched.uottawa.ca l At the Teaching Technologies home page, click on Course Webs (under Applications), then Science (a yellow box on the left). l Click on BIO 4118 - you’re there! l Obtain account for one of campus servers (e.g. for Proktor (Faculty of Science)). l Log on using your user ID and password, then launch Netscape. l Enter www.edteched.uottawa.ca in the location field.www.edteched.uottawa.ca l At the Teaching Technologies home page, click on Course Webs (under Applications), then Science (a yellow box on the left). l Click on BIO 4118 - you’re there!

11. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.11 Remote access l Launch Netscape and follow the same procedures, except... l...you will be asked for a user ID and password to get into the Course Webs home page. l UserID is bio4118; Password is Findlay. l Launch Netscape and follow the same procedures, except... l...you will be asked for a user ID and password to get into the Course Webs home page. l UserID is bio4118; Password is Findlay.

12. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.12 Components of Course Web l Syllabus and outline (Course info) l Lectures and summaries (PPT images) (Lectures) l Laboratory solutions (Laboratories) l Old exams & solutions, problems & solutions (Problem-solving) l Incidental info, commentary, responses to questions (What’s new) l Syllabus and outline (Course info) l Lectures and summaries (PPT images) (Lectures) l Laboratory solutions (Laboratories) l Old exams & solutions, problems & solutions (Problem-solving) l Incidental info, commentary, responses to questions (What’s new)

13. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.13 Lecture 1: The role of statistics in the scientific method l The hypothetico-deductive approach l Falsification of hypotheses l Evaluation criteria for scientific hypotheses l Uses of statistics l What statistics can do l What statistics can’t do l Selection criteria for statistical tests l The hypothetico-deductive approach l Falsification of hypotheses l Evaluation criteria for scientific hypotheses l Uses of statistics l What statistics can do l What statistics can’t do l Selection criteria for statistical tests

14. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.14 Some opinions of statistics “There are three types of lies: lies, damn lies, and statistics!” Benjamin Disraeli “If your experiment needs statistics, you should have done a better experiment.” Ernest Rutherford

15. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.15 Some opinions of statistics “To call in a statistician after the experiment is done may be no more than asking him to perform a postmortem “The purpose of models is not to fit the data, but to sharpen the questions.” Samuel Karlin examination; he may be able to say what the experiment died of.” Sir Ronald Fisher

16. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.16 The hypothetico-deductive approach Hypothesis Predictions ObservationsConclusions Question Inference Experiment DeductionInduction

17. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.17 Falsification of hypotheses l Scientific hypotheses can only be corroborated or falsified, not confirmed. l Hypotheses that have been rigorously tested come to be regarded as a fact, but should not be considered “true”. l Scientific hypotheses can only be corroborated or falsified, not confirmed. l Hypotheses that have been rigorously tested come to be regarded as a fact, but should not be considered “true”.

18. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.18 Evaluation criteria for scientific hypotheses l Generality l Accuracy l Precision l Simplicity l Generality l Accuracy l Precision l Simplicity

19. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.19 Hypothesis generality l A more general hypothesis eliminates more possibilities and applies to more situations. l e.g. in lakes, primary production is controlled by nutrient levels, versus l in small temperate lakes, primary production depends on the relationship between nutrient levels and consumption by zooplankton. l A more general hypothesis eliminates more possibilities and applies to more situations. l e.g. in lakes, primary production is controlled by nutrient levels, versus l in small temperate lakes, primary production depends on the relationship between nutrient levels and consumption by zooplankton.

20. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.20 Hypothesis accuracy l Two theories: y is a (1) linear or (2) non-linear function of x. l Observations are, on average, closer to predictions for the more accurate theory. l Two theories: y is a (1) linear or (2) non-linear function of x. l Observations are, on average, closer to predictions for the more accurate theory. Less accurate theory More accurate theory Observed Expected y x

21. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.21 Hypothesis precision l Two theories: H 1 : y is a linear function of x 1, or H 2 : y is a linear function of x 1 and x 2. l Since for given x, the difference between replicate measurements of y is smaller for H 2, it is the more precise theory. l Two theories: H 1 : y is a linear function of x 1, or H 2 : y is a linear function of x 1 and x 2. l Since for given x, the difference between replicate measurements of y is smaller for H 2, it is the more precise theory. More precise theory Less precise theory y x1x1 Y (corrected) x1x1 x2x2 Observed Expected

22. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.22 SimplicitySimplicity l Better hypotheses are simpler, easier to understand, or more economical or practical to use. l e.g. D = 15 W -1.16 l D = a + bW c + c sin(x 1 ) + fx 2 - gx 3 l Better hypotheses are simpler, easier to understand, or more economical or practical to use. l e.g. D = 15 W -1.16 l D = a + bW c + c sin(x 1 ) + fx 2 - gx 3

23. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.23 The uses of statistics l Provide a data summary l Help discover trends and patterns. l Evaluate magnitude and direction of experimental effects l Provide a data summary l Help discover trends and patterns. l Evaluate magnitude and direction of experimental effects l Assist in the design of experiments and field studies l A priori decisions about usefulness of experiments. l Assist in the design of experiments and field studies l A priori decisions about usefulness of experiments. l Evaluate biological hypotheses by testing to see whether observed patterns are consistent with predictions. DescriptionDesignHypothesis-testing

24. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.24 Use of statistics: inference l Are observed differences “real” or simply due to chance? l To answer this question, we need to know the probability that observed results are in fact due to chance. l Statistical tests allow us to estimate this probability and draw a conclusion. l Are observed differences “real” or simply due to chance? l To answer this question, we need to know the probability that observed results are in fact due to chance. l Statistical tests allow us to estimate this probability and draw a conclusion.

25. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.25 Use of statistics: description & synthesis l Provide a data summary. l Help discover trends (induction) through examination of summary statistics for patterns. l Remember: in statistical summaries, information is lost. So retain your raw data! l Provide a data summary. l Help discover trends (induction) through examination of summary statistics for patterns. l Remember: in statistical summaries, information is lost. So retain your raw data!

26. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.26 Use of statistics: experimental design l Allocation of effort l A priori decisions about usefulness of experiments l Allocation of effort l A priori decisions about usefulness of experiments

27. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.27 What statistics can and can’t do l provide objective criteria for evaluating hypotheses l help optimize effort l help you critically evaluate arguments l provide objective criteria for evaluating hypotheses l help optimize effort l help you critically evaluate arguments l tell the truth (probabilistic conclusions only!) l compensate for poor design l indicate biological significance: statistical significance does not mean biological significance, nor vice versa! CanCan’t

28. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.28 Four important questions to ask yourself before beginning any statistical analysis l Is there any reason to believe that your observations are independent and that in fact the data represent a “random sample”? And if so, random with respect to what? l Is it even possible to answer your question with the data you collected? l Can the contemplated analysis even answer your question, assuming there is an answer? l Are there alternate ways of analyzing the data? l Is there any reason to believe that your observations are independent and that in fact the data represent a “random sample”? And if so, random with respect to what? l Is it even possible to answer your question with the data you collected? l Can the contemplated analysis even answer your question, assuming there is an answer? l Are there alternate ways of analyzing the data?

29. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.29 The four ages of statistical man AgeDefining characteristicsComment StoneTotal ignoranceIgnorance is not bliss! BronzeNodding familiarity, but understanding purely superficial Statistics a (small) sidebar to scientific investigation (See Rutherford, Ernest) SilverModerate familiarity coupled with a strong desire to demonstrate same; statistical reach exceeds grasp Overwhelming concern with statistical minutae; scientific forest often obscured by statistical trees. GoldKnows when statistical issues are (and are not) important; recognizes limitations (of self and statistical science) That to which we can/should all aspire.

30. Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L1.30 Selection criteria for statistical tests l The question (hypothesis to be tested) and the nature of the data l The extent to which the assumptions of the test are met, and how sensitive the test is to violation of these assumptions l The power of the test l The question (hypothesis to be tested) and the nature of the data l The extent to which the assumptions of the test are met, and how sensitive the test is to violation of these assumptions l The power of the test