Understanding Statistics for Biological Data in Courses
Dive into the world of statistics for biological data through a comprehensive course led by experienced instructors at the University of Sheffield. Explore the fundamentals of statistics, research questions, hypotheses, and hypothesis testing related to biological inquiries. Gain insights into organizing, collecting, analyzing, and interpreting data in the context of biological studies. Enhance your statistical skills with a focus on practical applications and real-world scenarios to make informed conclusions.
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Statistics for biological data Statistics for biological data
Statistics for biological data Statistics for biological data Course Course instructors instructors Mark Dunning Bsc, Msc, PhD m.j.dunning@sheffield.ac.uk Aya Elwazir MBChB, Msc aymelwazir1@sheffield.ac.uk Timothy Freeman BA, MPhil tmfreeman1@sheffield.ac.uk
Statistics for biological data Statistics for biological data Course Course materials materials https://sbc.shef.ac.uk/stats-in-r/practical.nb.html
Statistics for biological data Statistics for biological data Introduction to statistics Course Course Objectives Objectives 1. Contingency tables & testing for categorical variables 2. Normality testing & Descriptive statistics 3. Testing for continuous variables Lots of practice!
Statistics for biological data Statistics for biological data Introduction Aya Elwazir Teaching assistant of medical genetics, FOMSCU PhD student, University of Sheffield
What is statistics? What is statistics? Organizing Collecting Analysing Interpreting
Research question & hypotheses Research question & hypotheses Does taking aspirin every day affect the risk of having a heart attack? Yes Yes No Daily aspirin does NOT affect risk of heart attack Daily aspirin affects risk of heart attack Daily aspirin reduces risk of heart attack Reject Two-tailed One-tailed Null hypothesis (H0) Alternative hypothesis (H1) Alternative hypothesis (H1)
Hypothesis testing: population & sample Hypothesis testing: population & sample Generalized Population Results Representative Random sample Independent Proper sample size Sample Type I & Type II errors Not Representative
Hypothesis testing: Type I & II errors Hypothesis testing: Type I & II errors Population (reality) H1: Aspirin affects risk H0: Aspirin doesn t affect risk (observed) Type I error (false positive) H1: Aspirin affects risk Type II error (false negative) H0: Aspirin doesn t affect risk Sample
Hypothesis testing: Type I & II errors Hypothesis testing: Type I & II errors Source: Effect Size FAQs
Hypothesis testing: P value Hypothesis testing: P value Statistics test the null hypothesis H0: Aspirin doesn t affect risk Assuming the null hypothesis is true How likely is an observed effect due to chance?? p = probability of chance Totally likely Totally unlikely 1 0.05 0 <5% probability that results are due to chance
Hypothesis testing: P value Hypothesis testing: P value Remember Statistical analyses can never prove the truth of a hypothesis, but rather merely provide evidence to support or refute it (Whitely & Ball, 2002) How is P value calculated? Significance tests Choice of test depends on type of data
Types of data Types of data Numeric Categorical Continuous Discrete Nominal Ordinal Severity Smoking status Age RBCs
Statistics for biological data Statistics for biological data Introduction to statistics Course Course Objectives Objectives 1. Contingency tables & testing for categorial variables 2. Normality testing & Descriptive statistics 3. Testing for continuous variables Lots of practice!
