Bio statistics is defined as the use of statistics in biological sciences. Statistics has been always used when one deal any issues at the population level. Thus Public Health, a profession that deals with population health, very much owes to statistics in its approaches to solve the problems of population health.
The basic understanding of the concepts and techniques in bio-statistics help the researcher identify the prospects and problems in research designs which is applicable for each context and the factors that need to be looked while operationalizing any study. Moreover, students doing a course that has enough scope for research will come across with several literature that have used techniques of bio statistics in its analysis whose familiarity in the mean time can help them use it on their own.
The basic understanding of the concepts and techniques in bio-statistics help the researcher identify the prospects and problems in research designs which is applicable for each context and the factors that need to be looked while operationalizing any study. Moreover, students doing a course that has enough scope for research will come across with several literature that have used techniques of bio statistics in its analysis whose familiarity in the mean time can help them use it on their own.
What are the basic things that a student can do using Bio statistics?
• Describe the role of bio-statistics in the discipline of public health
• Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions.
• Present statistical data using tabulation and making inferences giving due consideration to the basic measures of central tendency (Average) and dispersion.
• Apply the concepts of Probability, commonly used distribution patterns and tests of significance in research designs.
• Describe Sampling and its relevance in Research and able to use various kinds of sampling techniques
• Design small studies using the basic statistical techniques and will be able to infer the data meaningfully as well as to present it effectively.
Basic concepts in Bio statistics
Biostatistics in public health- Relevance, Types of data and types of variables –measurement scales, Frequency distribution: Discrete and continuous variables
Descriptive Statistics: Measures of central tendency and Measures of Dispersion
Measures of central tendency
Mean = sum of all the observations / the number of observations x =Sx /n
Eg: Find the mean of the following
3, 8, 2, 5, 10, 15, 9
Solution: Add each number
Sx = 52
n= 7
Mean = 52/7 = 7.43
Median = (n+1)/2 th observation
Eg: Find the median of the following?
3, 8, 2, 5, 10, 15, 9
3, 8, 2, 5, 10, 15, 9
Solution: arrange in the order of magnitude.
2,3,5,8,9,10,15
n= (7+1)/2
= 4th observation i.e 8
3,8,2,510,15,9,7
n= 9/2
= 4.5th observation i.e. 7.5
Mode – The most repeated observation.
Eg: Find the median of the following?
3, 8, 2, 5, 10, 15, 9, 2
Solution: Two times repeated observation is 2.
Problem 1: Calculate the various measures of central tendency for the following data of weight in k.g of 10 children aged 18-21 months.
11,12.5, 10, 10.8, 9.8, 13, 14.5, 14.2, 13.9, 15.1
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Measures of Dispersion: Range, Mean Deviation, Standard Deviation and Quartile Deviation
Presentation of data: Charts and Diagrams, Bar chart: Simple bar, Multiple, Component Bar, Histogram, Pie diagram, Line diagram.
Probability distributions-Binomial, Poisson, Gaussian (Normal) probability distribution; symmetry and skewness of distributions
Basic concepts of Sampling- Types of Sampling, Sampling error, Sample size estimations
Basic concepts of Statistical inference: statistical inferences confidence interval, Hypothesis testing type I and type II errors, p- value, statistical power and other statistics
Hypothesis testing
Hypothesis testing : Z tests: One sample and two sample tests for means
Hypothesis testing : Z test for proportions
Hypothesis testing: T- tests (Independent; paired t tests)
Hypothesis testing: Chi Square test, Exact test,
K sample case, Plots – scatter plots
Correlations: types of correlation , correlation coefficient
Regression- Linear and Logistic Regression
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