1. Postgraduate books recommended by Degree Management and Postgraduate Education Bureau, Ministry of Education Medical Statistics (the 2nd edition) 孙振球 主 编 徐勇勇 副主编

2. Arrangement: total 72 class hours, two classes each week

3. chapter 1 Introduction 1. Key definitions 2. the steps for medical statistics 3. Brief history of Statistics

4. Statistics  The science for data collection, sorting, and analysis.

5. Definition ： the science that study the collection, sorting and analysis of medical data. Characteristics ： 1 、 Using the quantity to reflect the quality 2 、 Using chance events (uncertainty) to reflect the inevitability (rules) Medical Statistics

6. Learning objectives ： 1 、 Basic principles and methods of Statistics （ Learning Emphasis ） 2 、 Application Statistics——(Clinical Medicine, Preventive Medicine, and Health Care Management) Medical Statistics

7. Purpose ： a tool for medical research Emphasis: statistical indicators used for calculating or comparing the quantitative characteristics of population Example: health expectation infant mortality Medical Statistics

8. Section 1. Key definitions

9. Ⅰ variable, individual, sample and population

10. individual （ observatory unit ）： the basic unit in statistical research, it depends on the purpose. variable （ indicator ）： individual characteristics examples: height 、 weight 、 gender 、 blood type 、 treatment effect etc.

11. Variable value ： the value of variables Examples: height 1.65 meters weight 52 kg gender female blood type “O” laboratory test negative treatment effect better Data: composed of a lot of variable values. Example: Data for blood glucose

12. homogeneity ： common characteristics for the given individuals example ： the heights of the boys with the age of 7 living in Changsha 2004 variation: difference existing among the variable values of homogeneity individuals example: the different heights of the boys with the age of 7 living in Changsha 2004

13. Definition ： the whole homogeneity individuals determined by specific purpose. example ： all the heights of boys at 7 that lived in Changsha 2004 finite population ： the space, time and population for a specific population have been limited. infinite population ： no time and space limits for the population. Such populations only exist in imagination, so it is called infinite population. population

14. definition ： the set of variable values of some individuals sampled from the population at random. Example: the heights of 200 boys at 7 from Changsha. sample

15. Sampling study Sample information (statistic) Population characteristics （ parameter ） inference note ： sampling is only the way to get information, inferring the population is our purpose

16. Ⅱ、 variable and data

17. measurement data: it is also called as quantitative or numerical data. Its value is quantitative. Measurement data always has measurement units. example ： height data, weight data

18. enumeration data: qualitative or count data. For such data, it needs to classify the observation units before and count them. Its value appear different characteristics and sorts.  Binomial: gender, live or death, yes or no.  Multiple ： blood type, A 、 B 、 O 、 AB.

19. ranked data: ordinal or semi-quantitative data. It need to classify observatory units into different classes according the extent before calculate the frequencies of each groups. There exists obvious differences among different classes. example: to evaluate the treatment effect of one drug on heart failure, we use the indicator (cured, better, worsen, dead) to assess the treatment effect. Choosing of statistical methods depends on the data type to a great extent 。

20. Data transformation Quantitative data ranked data （ multiple ） binomial data

21. example ： WBC （ 1/m 3 ） count of five persons ： 3000 6000 5000 8000 12000 quantitative variable lower normal normal normal higher qualitative variable Binomial data: normal 3 persons; abnormal 2 persons Multiple category data: lower 1 person; normal; 3 persons; higher 1 person

22. Ⅲ error

23. definition ： the difference between measurement value and true value. 1 、 rand error ： unstable and changing at random errors that caused by uncontrolled factors. Commonly, rand errors are referred to those errors appearing during repeated measurements and sampling. Often, measurement error is extremely lower than sampling error. In Statistics, sampling error is the main study contents.

24. 2. Nonrandom error is divided into systematic error and non systematic error: Systematic error: it is produced in experiment and keeps constant or changes according certain rules. Usually, its reasons are known and controllable. Nonsystematic error （ gross error ） : it is always caused by obvious grosses.

25. Ⅳ、 frequency and probability

26. 1 ． Frequency Given the same condition, repeat a trial for n times independently. Among n trials, A appears for m times ， so the ratio of m/n is called the frequency of random event A among n trials.

27. 2 ． probability: the likelihood of random events. Given the same condition, repeat a trial for n times independently. Among n trials, A appears for times ， so the ratio of is called the frequency of random event A. As n increases gradually, the frequency will approach a constant. The constant is called the probability of random event A and expressed in. In common, it is abbreviated as.

28. Range ：

29. Frequency is used to describe the sample, while the probability for the population. m/n is the estimation of. As trials increases, the estimation value is more reliable.

30. small probability event: Because the conclusions are made based on a certain significance level, statisticians always select as judge criterion. So such events with are called small probability events. It means that such events happen rarely and can be regarded as nonoccurrence.

31. Section 2 the steps for statistical work

32. Here, it means statistical design, the most important factor for a successful research. It involves the arrangements for the process of data collection, sorting and analysis. Ⅰ design

33. 3 ． control Three principles for experiment design 1 ． randomization 2. Replication

34. objective ： to gather accurate and reliable raw data data sources ： ① statistical reporting ② routine records ③ purposive surveys or experiments ④ statistical yearbook and special data book requirements ： 1 、 randomization 2 、 sufficient sample size Ⅱ Data collection

35. Ⅲ． Data sorting It is the process that cleans and systematizes raw data. Data sorting prepares the required data for next step, data analysis.

36. Ⅳ Data/statistical analysis objective ： to illustrate the rules hidden in the data. It includes two aspects: 1. statistical description ： it is the process of describing the characteristics of data using statistical indicators, statistical charts and statistical tables. 2. statistical inference ： the process of using sample statistic to infer population parameter. It consists of: parameter estimation and hypothesis testing.

37. Statistical description Statistical inference indicator Table and chart Parameter estimation Hypothesis testing Statistical analysis