BIOL2300Biostatistics

Prof. Peter CloteBiology, Boston College

Fall Semester 2018MWF 9:00-10:00 in Higgins 310

Course Mechanics

• TA: Rayane Dennaoui, Biology graduate student

• Emails: clote@bc.edu, dennaoui@bc.edu

• Course webpage – google “Clote Lab”, then follow link to “Current Courses”

• Direct linkhttp://clavius.bc.edu/~clote/courses/BIOL2300

• Password to notes and homeworks: “biol2300”

Course Mechanics• Office Hours for Prof Clote

• Mon 1-2 pm, Wed 12-1, Fri 10-11 in Higgins 577

• Tel: 617 552-1332

• Email: clote@bc.edu

• Office Hours for Rayane Dennaoui

• Tue 4:30-5:30 pm, Thur 3:30-5:00 in Higgins 577.

• Tel: 857-800-3460

• Email: dennaoui@bc.edu

Course Mechanics

Grade Determination

– 10%: Weekly homeworks, class participation– 10%: unannounced quizzes– 50%: two midterms (Oct 10 and Nov 21)– 30%: Final Exam Fri Dec 14 at 9:00 am in Higgins 310.

– possible modification of grading policy during semester –if so, you’ll be notified

• Textbook: Biostatistics for the Biological and Health Sciences, by Marc M. Triola and Mario F. Triola and Jason Roy. Pearson, Addison Wesley, ISBN 978-0-13-403901-5(hardcover), ISBN 978-0-13-403901-7 (softcover), 2nd edition

• Since 2006, Triolo-Triola and now Triolo-Triolo-Roy is one of the most widely used biostatistics books in US

• STATISTICAL CALCULATOR needed for tests and final examination

• Excel and Mathematica for in-class demos and (optionally) homework

• Download site for EXCEL: http://www.bc.edu/software/applications/office.html

Course Mechanics

• Homework collection policy: hardcopy ONLY, stapled, with homework assignment number, your name and date. NO EMAILED homework!!!

• No late homework.• ACADEMIC INTEGRITY POLICY

– See statement on course webpage– Any infringement will be turned over to the

Academic Integrity Board

Course Mechanics

Course goals• Descriptive statistics, probability theory (binomial,

hypergeometric, Poisson, normal distributions), hypothesis testing, correlation and regression, non-parametric statistical tests

• Use Excel and Mathematica for statistical analysis.

• Comprehend, critique, and communicate research findings from biomedical literature.

Caveats• BIOL2300 requires differential and integral

calculus to really understand concepts, and easily remember formulas; however calculus is not necessary to do any of the problems in statistics.

• Class mandatory

• Please turn OFF phones!

Florence Nightingale1820-1910

Rose diagram introduced by F. Nightingale

Statistical Analysis of Circular Data By N. I. Fisherhttp://books.google.com/books

Ludwig Boltzmann1844-1906

• �Nothing is more practical than a good theory.�

Statistics is routinely misunderstood or incorrectly used in medicine and health sciences!

• �In a study by (Anthony, 1996) the use of statistics in papers from high-quality medical research journals were analyzed. He reports that statistical errors and misunderstandings of statistical concepts are almost the norm rather than the exception. Errors were found in more than 45% of all papers reviewed.”

• D. Anthony (1996). �A review of statistical methods in the journal of advanced nursing�, J Adv Nurs 24(5): 1089-1094.

Statistics provides a rigorous framework in which to analyze and understand biological data

Statistics matters!

Ethical misconduct in medical and pharmacological research may

misuse statistics to falsely portray benefits of new procedure or drug.

Definitions• data: observations (measurements), may be

qualitative or quantitative• census: collection of data from each member

of population• sample: collection of data from (small) subset

of population• parameter: numerical summary of values for

entire population (fixed number, often not known exactly)

• statistic: numerical summary of values based on a sample (generally different samples produce different summary numbers)

Type of data

Example of statistics and parameters

Discrete vs continuous• discrete data: values from either a finite or

countably infinite set – in survey of 100 students, 37 liked Starbuck�s

coffee• continuous data: values from a real interval

or union of real intervals– average internal energy in gas at given

temperature

Current hype around “Big Data”

Experiment vs. observation

Retrospective versus prospective study

• cross-sectional study: data are observed/collected at one point in time = NOW

• retrospective study: data are collected by going back in time = PAST

• prospective study (cohort) study: data are collected in future from groups, called cohorts = FUTURE

Experimental study

Observational study

Another example of observational study

Example

Sources of error

• sampling error: difference betwen sample result and true population result

• nonsampling error: data incorrectly collected (eg sampling bias, nonresponse bias, response bias)

Simple random sampling• Throughout course, we will assume that

sampling is �simple random sampling� with no bias.

• The manner in which polling is done is IMPORTANT. On Jan 10, 2017, the major French newspaper, Le Parisien, announced that it will no longer pay Ipsos for polling surveys on political races (in the past, Le Parisien had spent ~$30 million per year for polls).