This document provides an introduction and overview of biostatistics. It defines key biostatistics terms like population, sample, parameter, statistic, quantitative vs. qualitative data, levels of measurement, descriptive vs. inferential biostatistics, and common statistical notations. It also discusses sources of health information and how computerized health management information systems are used to collect, analyze and report data.

1. E LEMENTARY Chapter 1 Introduction to Bio-Statistics Chapter 1 Introduction to Statistics

3. DATA:::DISCRETE OSERVATIONS OF ATTRIBUTES OR EVENTS THAT CARRY LITTLE MEANING WHEN CONSIDER ALONE REDUCE,SUMMERISE ADJUSTING FOR VARIATION  INFORMATION TRANSFORMATION OF INFORMATION THROUGH INTEGRATION AND PROCESSING WITH EXPERIENCE AND PERCEPTION BASED ON SOCIAL AND POLITICAL VALUE  INTELLIGENCE

4. STATISTICS S - Scientific method for T - tabulation A - analysis T - testing of hypothesis and I - inference S - study of T - time trend I - in C - community S - set up

5. Statistics Two Meanings Specific numbers Method of analysis Statistics 1-1 Overview Specific number numerical measurement determined by a set of data Example: Twenty-three percent of people

6. Specific number numerical measurement determined by a set of data Example: Twenty-three percent of people Statistics

7. Method of analysis a collection of methods for planning experiments, obtaining data, and then then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data Statistics

8. Biostatistics is the application of statistical methods to the problems ofbiology, including human biology, medicine and public health Descriptive Biostatistics : It is the study of biostatistical procedures which deal with the collection, representation, calculation and processing. i.e., the summarization of data to make it more informative and comprehensible. It involves graphical and tabular to describe. Includes: Collecting Organizing Summerizing Presenting data Inferential Biostatistics: It constitutes the procedures which serve to make generalizations or drawing conclusions on the basis of the studies of a sample. This is also known as sampling biostatistics. Includes: Making inferences Hypothesis testing Determining relationship Making predictions

9. Statistical significance - whether the effect is due to by chance or real Experimental ->Intervention ->Outcome Study Analysis -> Inference Observational -> Exposure ->Observation Introduction Population the complete collection of all elements (scores, people, measurements, and so on) to be studied. The collection is complete in the sense that it includes all subjects to be studied.

10. Definitions Population the complete collection of all elements (scores, people, measurements, and so on) to be studied. The collection is complete in the sense that it includes all subjects to be studied.

11. Definitions Census the collection of data from every element in a population Sample a subcollection of elements drawn from a population

12. Parameter a numerical measurement describing some characteristic of a population Definitions

13. Parameter a numerical measurement describing some characteristic of a population population parameter Definitions

14. Definitions Statistic a numerical measurement describing some characteristic of a sample

15. Definitions Statistic a numerical measurement describing some characteristic of a sample sample statistic

16. Definitions Quantitative data numbers representing counts or measurements

17. Definitions Quantitative data numbers representing counts or measurements Qualitative (or categorical or attribute) data can be separated into different categories that are distinguished by some nonnumeric characteristics

18. Definitions Quantitative data the incomes of college graduates

19. Definitions Quantitative data the incomes of college graduates Qualitative (or categorical or attribute) data the genders (male/female) of college graduates

20. Discrete data result when the number of possible values is either a finite number or a ‘countable’ number of possible values 0, 1, 2, 3, . . . Definitions

21. Discrete data result when the number of possible values is either a finite number or a ‘countable’ number of possible values 0, 1, 2, 3, . . . Continuous (numerical) data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps. Definitions 2 3

22. Discrete The number of eggs that hens lay; for example, 3 eggs a day. Definitions

23. Discrete The number of eggs that hens lay; for example, 3 eggs a day. Continuous The amounts of milk that cows produce; for example, 2.343115 gallons a day. Definitions

24. nominal level of measurement characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as low to high) Example: survey responses yes, no, undecided Definitions

25. ordinal level of measurement involves data that may be arranged in some order, but differences between data values either cannot be determined or are meaningless Example: Course grades A, B, C, D, or F Definitions

26. interval level of measurement like the ordinal level, with the additional property that the difference between any two data values is meaningful. The distance is defined. But ratio is not defined. Not include the natural zero starting point (where zero indicates that none of the quantity is present). Example: temp in centigrade, intelligence score 20-25 0 c =30-35 0 c………0 0 c is not mean the absence of heat…20 0 c is not twice as hot as 10 0 c Definitions

27. ratio level of measurement the interval level modified to include the natural zero starting point (where zero indicates that none of the quantity is present). For values at this level, differences and ratios are meaningful. The distance and ratio defined. Example: length, weight…100 cm is 50cm more than 50 cm or twice as long as 50 cm Definitions

28. Nominal - categories only Ordinal - categories with some order Interval - differences but no absolute zero. The distance is defined. But ratio is not defined. Ratio – differences…….. absolute zero. The distance and ratio are defined. Levels of Measurement qualitative quantitative

29. Bio - Statistics Descriptive (Summarize & Describe data) Inferential ( draw conclusion) Qualitative Quantitative Estimation Hypothesis Testing Confidence Interval P Value Proportion, Percentage Rate, Ratio Central tendency (mean median, mode) Dispersion Standard deviation standard error mean variance

30. Common Statistical Notations & Symbols Summery value Sample statistics Popln. parameter Mean X µ Standard Deviation S σ Variance S 2 σ 2 Proportion p P Component of proportion q Q Other Commonly Used Symbols: Z : No of SD from Mean or standard normal deviate/ variate d.f or f. : degree of freedom P value : : Probability value

31. DATA  REDUCE,SUMMERISE ADJUSTING FOR VARIATION  INFORMATION COMPONENT OF HEALTH INFORMATION Demography Environmental Health Statistics Health Status :Mortality, Morbidity, Disability, Qol Health Resources Utilization Non Utilization Health Services USES OF HEALTH INFORMATION::: Measure health status  local national and international comparison Quantify health problems Planning and effective management of health care need Assessing health service accomplishing their objectives or not

32. ::::SOURCES OF HEALTH INFORMATION::::: Census Registration Of Vital Events:: Birth 14 Days, Death 7 Days. Srs Notification Of Disease Hospital Records Disease Registers Epidemiological Surveillance Environmental Health Information Health Manpower Statistics Population Surveys

33. Health Management Information System (HMIS) A computerised information system (HMIS) has been installed in nearly all districts in India . Data from SC, PHC, CHC and Hospitals is available for reporting, supervision, planning and analysis BEFORE Summary of data was calculated by hand and therefore prone to errors Long delay to produce reports

34. Computerized HMIS Data Collection at Health Facilities Form 6, 7 and 8) District Computer Unit Block Computer Unit Decision Support System State Directorate Health Managers / Program Officers Through Floppy Through FTP, using phone lines