Biostatistics and data analysis
- 1. BIOSTATISTICS AND DATA ANALYSIS BY DAVID O. ENOMA. 1
- 2. DEFINITIONS Biostatistics is the branch of applied statistics directed toward applications in the health sciences and biology. Biostatistics is sometimes distinguished from the field of biometry based up on whether applications are in the health sciences (bio statistics) or in broader biology (biometry) e.g agriculture, ecology, wildlife biology. 2
- 3. FUNDAMENTAL TOOLS OF THE SCIENTIFIC METHOD Hypothesis Formulation Experimental design and observational studies Data gathering Data summarization/representation Drawing inferences 3
- 4. 4 Data are the quantities (numbers) or qualities (attributes) measured or observed that are to be collected and analysed.
- 5. 5 A dataset is a collection of data. For example a dataset may contain measurements and observed attributes on 100 low birth weight infants born in two teaching hospitals. Systolic BP, gender, toxaemia, gestational age etc. CONSTANTS VARIABLES
- 7. TYPES OF VARIABLES QUALITATIVE These are variables that have values that are intrinsically nonnumeric. This means that they are categorical. They can be nominal or ordinal. E.g. Nationality, race & gender. QUANTITATIVE These are variables that have values that are intrinsically nonnumeric They can be discrete or continuous. Number of pregnancies & Duration of a seizure. 7
- 8. GOALS OF DATA ANALYSIS To describe (summarize) the population of interest by describing what was observed in the (study) sample. To use patterns in the (study) sample data to draw inferences about the population represented. 8
- 9. DESCRIPTIVE STATISTICS Descriptive statistics are concerned with the presentation, organization and summarization of data.(Norman & Streiner, 2008) In simple terms, describing data. Numerical presentation Graphical presentation Mathematical presentation 9
- 10. NUMERICAL PRESENTATION This describes the number of data that falls under a particular category. Tools include frequency tables and polygons. 10
- 11. GRAPHICAL PRESENTATION These include Line graph Frequency polygon, Frequency curve, Histogram, Bar graph, Scatter plot. 11
- 12. 12 LINE GRAPH Time series of maternal mortality rates. FREQUENCY POLYGON Distribution of 45 patients at (place) , in (time) by age and sex
- 13. MATHEMATICAL PRESENTATION Mathematical presentation is also referred to as summary statistics. MEASURE OF LOCATION This type of measure is useful for summarizing date and it defines the centre or middle of the sample values. (Rosner, 2010). Mean, Median and Mode. (central tendency). Quartiles and percentiles. (no central tendency). MEASURES OF SPREAD This measures the degree of variability in the data. Variance, Standard Deviation, Standard Error of the mean . 13
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- 15. 2 i=1 n i 2 s = (x - x) n -1 15 S = standard deviation (square root of variance)
- 16. Empirical Rule For a Normal distribution approximately, 68% of the measurements fall within one standard deviation around the mean 95% of the measurements fall within two standard deviations around the mean 99.7% of the measurements fall within three standard deviations around the mean 16
- 18. INFERENTIAL STATISTICS Inferential statistics allow us to generalize from our sample of data to a larger group of subjects. Accuracy of inference depends on representativeness of sample from population Inferential Statistics uses sample data to evaluate the credibility of a hypothesis about a population Calculation of mean difference. 18
- 19. STEPS IN INFERENTIAL STATISTICS State Hypothesis Ho: No difference between 2 means; any difference found is due to sampling error Level of Significance level of probability or level of confidence Probability that sample means are different enough to reject Ho (.05 or .01) 19
- 20. Computing calculated value used for testing for mean differences. 20
- 21. THANK YOU. 21
- 22. REFERENCES Norman, G. R., & Streiner, D. L. (2008). Biostatistics: The Bare Essentials: B.C. Decker. Rosner, B. (2010). Fundamentals of Biostatistics: Cengage Learning. 22