biostatistics basic
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This document provides an introduction to biostatistics. It defines key biostatistics terms like data, variables, scales of measurement, and methods of data presentation. Descriptive and inferential statistics are introduced. Common measures of central tendency (mean, median, mode) and dispersion (range, standard deviation, variance) are defined for different data types. Common methods for presenting data visually, like histograms, bar graphs and box plots, are also described. The normal distribution is introduced as an important assumption for many statistical tests. Examples are provided to illustrate concepts like using z-scores to determine what proportion of values fall above or below a given cutoff from the mean.
biostatistics basic
- 1. AN INTRODUCTION ON BIOSTATISTICS PART 1 Dr. Vaneet Aggarwal Department of Pharmacology 1
- 2. TOPICS : Definitions Data and types of data Methods of data presentations Types of stats Measure of central tendency Measure of dispersions The normal distribution 2
- 4. STATISTICS: Principles and methods for collection, presentation, analysis and interpretation of data. BIOSTATISTICS: Tools of statistics applied to the data that is derived from biological sciences. 4
- 5. WHY DO WE NEED BIOSTATISTICS ? To define normalcy. To study the correlation or association between two or more attributes. Locate define and measure the extent of the disease. To evaluate the efficacy of drugs. To determine the success or failure of health care program. 5
- 6. QUALITY RESEARCH How does biostatistics help a post graduate student ? 6
- 7. DESCRIPTIVE AND INFERENTIAL STATISTICS Descriptive stats are concerned with the presentation, organisation, and summarization of data. Inferential stats allow us to generalise from our sample group of data to a larger group of subjects. 7
- 8. VARIABLES A variable is simply what is being observed or measured. 8
- 9. FLAVOURS OF VARIABLES DEPENDENT variable is the outcome of interest, which should change in response to some intervention. INDEPENDENT variable is the intervention, or what is being manipulated. 9
- 10. More generally if one variable changes in response to another, we say that dependent variable is the one that changes in response to independent variable. 10
- 13. 13 DATA QUALITATIVE DATA (Discrete/ frequency data) QUANTITATIVE DATA (Continuous data) Subjects with same characteristic are counted to form specific groups. They have magnitude.
- 15. 15 NOMINAL SCALE ORDINAL SCALE INTERVAL SCALE RATIO SCALE Non Categorical Categorical SCALES OF DATA MEASUREMENT
- 16. NOMINAL SCALE A nominal variable consists of named categories, with no implied order among the categories. "Existential" variables 16
- 17. ORDINAL SCALE An ordinal scale consists of ordered categories, where the difference between categories cannot be considered to be equal. 17
- 18. INTERVAL SCALE An interval scale has equal distances between values, but the zero is arbitrary. 18
- 19. RATIO SCALE A ratio scale has equal interval between values and a meaningful zero point. 19
- 20. SCALES AT GLANCE 20 Scale type Assumptions Nominal NAMED categories Ordinal Ordered categories Interval Equal intervals Ratio Meaningful zero
- 21. EXAMPLES OF SCALES Indicate whether the following variables are nominal, ordinal, interval or ratio. a) your income (assuming it's more than $0). b) a list of the different specialities in your profession. c) the ranking of specialities with regard to income. d) Salman Khan was described as a "10". What type of variable was the scale? e) a range of motion in degrees. 21
- 22. EXAMPLES OF SCALES f) a score of 13 out of 17 on the Anxiety Scale. g) staging of breast cancer as type I, II, III or IV. h) ST depression on the ECG, measured in millimeters. i) ST depression, measured as "1"+/- 1mm, "2"= 1 to 5mm, and "3" </= 5mm. j) ICD-9 classifications: 0295=organic psychosis, 0296=depression and so on. k) diastolic blood pressure , in mm Hg. l) pain measurement on a seven-point scale 22
- 23. PROPORTION AND RATE A proportion is a type of fraction in which the numerator is subset of the denominator. Rate is a fraction that also has a time component. 23
- 24. DATA REPRESENTATION Graphing for nominal data Graphing the ordinal data Graphing the interval and ratio data 24
- 25. NOMINAL DATA Bar graphs Histograms Dot plots 25
- 26. EXAMPLE 26 COURSE NUMBER OF STUDENTS FORENSIC 25 PSM 42 PHARMACOLOGY 8 MICROBIOLOGY 12 PATHOLOGY 13
- 27. 27 0 12.5 25 37.5 50 forsensic psm pharma micro patho
- 28. 28 0 12.5 25 37.5 50 psm forensic patho micro pharma
- 29. 29 0 12.5 25 37.5 50 psm forensic patho micro patho
- 30. ORDINAL DATA Bar graphs Histograms Dot plot 30
- 31. INTERVAL AND RATIO DATA Histograms Stem and leaf plot Frequency polygon Cumulative frequency polygon 31
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- 36. FOR MAKING HISTOGRAMS Rank order the data Find the range Choose the width New table giving you interval midpoint count etc Turn into histogram Lose some info on the way 36
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- 38. 38
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- 43. HISTOGRAM FOR 3 GROUPS 43
- 44. FREQUENCY POLYGON FOR 3 GROUPS 44
- 45. SO WHEN TO USE WHAT ? Bar graphs and Histograms can be used for all types of data. But when more than 2 groups than frequency polygon. Use graphs to show relationships not to report numbers. 45
- 46. 46 0 12.5 25 37.5 50 psm forensic patho micro pharma
- 48. 48 0 100 200 300 400 April May June July
- 49. TABLE 1 49
- 52. NUMBERS A specific data point - the value of variable for one subject is represented by capital letter X. We denote the mean of a variable by putting a bar over the capital letter X: X The number of subjects in the sample is represented by N. n indicates the sample size of a group. Use subscript notation to differentiate between various sample sizes data points etc. 52
- 53. NUMBERS Nominal data Ordinal data Ratio and interval data 53
- 54. 54
- 55. INTERVAL AND RATIO DATA Mean is the measure of central tendency. A measure of central tendency is the typical value for the data. 55
- 56. ORDINAL DATA Median is the measure of central tendency. The median is that value such that half of the data points fall above it half below it . 56
- 57. NOMINAL DATA Mode is the measure of central tendency. The mode is the most frequently occurring category. 57
- 58. MEASURE OF DISPERSION Refers to how closely the data cluster around the measure of central tendency. 58
- 59. NOMINAL / ORDINAL DATA Index of dispersion 59
- 60. ORDINAL DATA Range (diff between highest and lowest values) Interquartile range / midspread 60
- 61. 61
- 62. INTERVAL OR RATIO DATA Interquartile range The mean deviation The variance and the standard deviation The coefficient of variation 62
- 66. 66
- 67. BOX PLOTS 67
- 68. N/C: SETTING THE SCENE A survey of schools found that the most widely used method to get out of going to school is "not today mom I have a headache". Based on the survey of 2000 student it was found to be used an average of 100 times a year with a SD of 15. Can we determine what proportion of students use this reason at least 115 times a year; or fewer than 70 times a year; or between 106 and 112 times annually ? 68
- 70. NORMAL DISTRIBUTION Assumption for statistical tests Mean and variance independent All natural phenomenon Distribution of means 70
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- 73. NORMAL CURVE Mean, median, mode Symmetrical Approaches the x axis asymptotically 73
- 74. HOW MANY STUDENTS 115 TIMES ? 74
- 75. FEWER THAN 70 TIMES IN A YEAR ? 75
- 76. BETWEEN 106 -112 TIMES ? 76
- 77. 77
- 78. –Winifred Castle “Most researchers use statistics the way a drunkard uses a lamp-post more for support than illumination.” 78
Editor's Notes
- #6: Also for planning and administration of health care services. Predicting the trends of the diseases. To assess health status of population. To understand the association between cause and effect in various diseases. To assess the efficacy of various treatments for those diseases.
- #23: Ordinal/ interval Staging is ordinal St depression is ratio Grouping ordinal List diagnosis is nominal Bp is ratio Ordinal treated as interval
- #53: M bar IS also used To add series of numbers we use uppercase Greek letter sigma