Statistics

1. Statistics “There are three kinds of lies: lies, damned lies and statistics”

2. # 4 out of 5 dentists recommend Dentyne # Almost 85% of lung cancers in men and 45% in women are tobacco-related. # Condoms are effective 94% of the time. # Native Americans are significantly more likely to be hit crossing the streets than are people of other ethnicities. # People tend to be more persuasive when they look others directly in the eye and speak loudly and quickly. # Women make 75 cents to every dollar a man makes when they work the same job. # A surprising new study shows that eating egg whites can increase one's lifespan. # People predict that it is very unlikely there will ever be another baseball player with a batting average over 400. # There is an 80% chance that in a room full of 30 people that at least two people will share the same birthday. # 79.48% of all statistics are made up on the spot.

3. Statistics are everywhere! • Health • Food • Sports • Politics • Science

4. Misuse of statistics • Discarding unfavourable data • Loaded questions • Overgeneralisation • Biased samples • Misreporting / misunderstanding error • False causality • Proof of null hypothesis • Data dredging • Data manipulation

5. Statistics in science

6. Spreadsheets..  • Numerical bugs • Problems with blank cells • Typing mistakes • Circular references • The beginning of time (1900) • Bad graphics • Poor statistics

7. Spreadsheets • Excel  • Openoffice  http://www.openoffice.org/ • Gnumeric  http://projects.gnome.org/gnumeric/

8. P < 0.05 • In statistical hypothesis testing, the p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. • The lower the p-value, the less likely the result, assuming the null hypothesis, so the more "significant" the result, in the sense of statistical significance. One often rejects a null hypothesis if the p-value is less than 0.05 or 0.01, corresponding to a 5% or 1% chance respectively of an outcome that extreme, given the null hypothesis.

9. misunderstandings • The p-value is not the probability that H0 or H1 are true • The p-value does not indicate the size or importance of the observed effect • Biological vs. statistical significance