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# Basics in Epidemiology & Biostatistics 2 RSS6 2014

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### Basics in Epidemiology & Biostatistics 2 RSS6 2014

1. 1. Basics in Epidemiology & Biostatistics Hashem Alhashemi MD, MPH, FRCPC Assistant Professor, KSAU-HS
2. 2. • Large samples > 30. • Normally distributed. • Descriptive statistics: Range, Mean, SD. Non-parametric data • For small samples & variables that are not normally distributed. • No basic assumptions (distribution free). • Descriptive statistics: Range, Rank, Median, & the interquartile range. (the middle 50 = Q3-Q1). • Median is the middle number in a ranked list of numbers (regardless of its frequency). Parametric data
3. 3. Non-parametric data
4. 4. The Mean • It sums all the values (great digital summary ). • But, it will be affected by extreme values. So, it is not a good summary if your data is not normal (symmetrical bell shape). • The sum of data differences above and below the mean will equal = 0. "‫الوسط‬ ‫األمور‬ ‫خير‬ ‫شطط‬ ‫التناهي‬ ‫حب‬"
5. 5. Stander Deviation Average of differences from the mean (Squared-SS) Sample set: 1 ,2 ,3 ,4 , 5 ,6 ,7 X = 28/7= 4 Number of differences = 6 (n-1) Stander deviation Unit of deviation of data from the Mean
6. 6. Differences?
7. 7. Similar < +/- 1𝛔 Slightly Different Very Different Extremely Different (0.02) > +/-2𝛔 (0.001) >+/- 3𝛔 <+/-2𝛔
8. 8. • Zdistribution, is a hypothetical population (model) with a 𝛍 of 0, & 𝛔 1. • Six (𝛔 ) make up 0.997 of the area under the curve Z distribution Parametric Data Population %
9. 9. • God knows every thing. • Dose not need to take samples. • Commits no mistakes.
10. 10. Central Limit Theorem • The mean of all possible sample means will be approximately equal to the mean of the population. • The distribution of all possible sample means will be normal. • If you limit your prediction to the center, you will be ok (averages are normally distributed) (1777 – 1855) "‫الوسط‬ ‫األمور‬ ‫خير‬ ‫شطط‬ ‫التناهي‬ ‫حب‬" Carl Friedrich Gauss
11. 11. • tdistribution, is a hypothetical population (model) with a 𝛍 of 0, & 𝛔 1 , (Degrees of freedom= n-1). • Six (𝛔 ) make up 0.997 of the area under the curve t distribution Parametric Data Sample Sampling distribution %
12. 12. Similar <+/-1 SE Slightly Different Very Different Extremely Different (0.02) > +/-2 SE (0.001) >+/- 3 SE <+/-2 SE
13. 13. Stander Error SE is the unit for error in estimating the population mean. SE is the unit for deviation of all possible samples means from the population mean. SE is the unit for average difference of all possible samples means from population mean. n because S is a root product of the variance.
14. 14. The Average Idea SE Stander ErrorS Stander DeviationX mean A unit for Error in estimation of the population mean. A unit of Deviation of the data from the sample mean. Average A unit for Deviation of all possible samples means from the population mean. A unit for Average of differences of the data from the sample mean. A unit for Average of differences of all possible samples means from population mean.
15. 15. A Fancy World made of %s & Averages Biostatistics
16. 16. Sample size Estimate Calculate Calculate (SE) ? ? Estimate
17. 17. 95% Confidence Interval (C.I) SE Stander of Error +/- 2 SE μ π Ω λ Estimate Margin of Error X P OR Rate General formula
18. 18. SD vs SE • Standard Deviation calculates the variability of the data within a sample in relation to the sample mean . • Standard Error estimates the variability of all possible samples means in relation to the population mean. So, it helps identify the % of data above and below a certain measurement. So, it helps identify the degree of error in your estimation.
19. 19. A Fancy World made of Biostatistics Averages & %s
20. 20. Population (descriptive) : • Calculate Mean μ (measures) • Calculate proportion 𝛑 (counts) • Calculate Stander deviation σ • Calculate Parameters: μ & 𝛑 Sample (Inferential) : • Estimate Sample size • Calculate Mean X • Calculate Stander deviation S • Calculate Stander error SE & 95% C.I (Confidence Interval) • Calculate Statistics Difference between studying populations & samples: Estimate Parameters: μ & 𝛑
21. 21. END
22. 22. • Large samples > 30. • Normally distributed. • Descriptive statistics: Range, Mean, SD. Non-parametric data • For small samples & variables that are not normally distributed. • No basic assumptions (distribution free). • Descriptive statistics: Range, Rank, Median, & the interquartile range. (the middle 50 = Q3-Q1). • Median is the middle number in a ranked list of numbers. Parametric data
23. 23. Non-parametric data • For small samples and variables that are not normally distributed. • No basic assumptions (distribution free). • Descriptive statistics: Range, Rank, Median, and the interquartile range (the middle 50 = Q3-Q1).
24. 24. Count Quantitative Data Discrete Continuous Binomial (Binary) : Sex Ratio (real zero) / Interval (no zero) Temperature/BP Multinomial : 1-Categorical : Race 2-Ordinal: Education 3-Numerical: number pregnancies/residents Measure
25. 25. Non-parametric data • For small samples and variables that are not normally distributed. • No basic assumptions (distribution free). • Descriptive statistics: Range, Rank, Median, and the interquartile range (the middle 50 = Q3-Q1).
26. 26. Differences?
27. 27. Objectives • Definitions. • Types of Data. • Data summaries. • Mean Χ , Stander deviation S. • Stander Error SE, Confidence interval C.I of μ .
28. 28. Quantitative Data Discrete Continuous Dichotomous: Binary: Sex Multichotomous: 1-No order : Race 2-Ordinal: Education Numerical: number pregnancies/residents Ratio (real zero) / Interval (no zero) Temperature/BP (Non-Parametric Data)
29. 29. Quantitative Data Discrete Continuous Categorical : 1- Di-chotomous: Sex 2- Multi-chotomous: Race,Education Numerical: number of pregnancies/residents Ratio (real zero) / Interval (no zero) Temperature/BP Types of Data Count Non-Parametric Data Parametric Data Parametric Data
30. 30. Summaries Visual Numerical X, 𝛍, s, 𝛔Histogram P, 𝛑, s, 𝛔Bar & Pie Chart (Counts) Categories (Measures) Any value
31. 31. Data Presentation %%
32. 32. Normality & Approximation to Normality Why?
33. 33. Approximation to Normality • If choices are equally likely to happen • If repeated numerous number of times • It will look normal. • Whether it was a coin or a dice (Di-chotomous or Multi-chotomous)
34. 34. Normality & Approximation to Normality Clinical Relevance?
35. 35. Choices equally likely to happen….. i.e. Out come of interest probability is unknown (Research ethics) Repeated numerous number of times…. i.e. Large sample size Normality assumption helps us predict the Probability of our outcome
36. 36. The Bell / Normal curve Stander deviation(SD)/ sample curve True error (SE)/ population curve • Was first discovered by Abraham de Moivre in 1733. • The one who was able to reproduce it and identified it as the normal distribution (error curve) was Gauss in 1809.
37. 37. De Moivre had hoped for a chair of mathematics, but foreigners were at a disadvantage, so although he was free from religious discrimination, he still suffered discrimination as a Frenchman in England. Born 1667 in Champagne, France Died 1754 in London, England
38. 38. Largest Value - Smallest Value SD estimate