“Biostatistics and Research Methodology” authored by R M Ganbawale.(NCBA)

Name: "Biostatistics and Research Methodology" authored by R M Ganbawale.(NCBA) - Booksbp.com
SKU: 9788173816413
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SKU: 9788173816413

Part 1: Research Methodology

Introduction to Research: Definition, types of research (fundamental, applied, clinical), and the scientific method in medicine.
Research Problem & Design: Formulating a research question, hypothesis development, and choosing an appropriate study design (observational vs. experimental).
Epidemiological Designs: Structure of cross-sectional studies, case-control studies, cohort studies, and randomized controlled trials (RCTs).
Literature Review: Techniques for systematic search, critical appraisal of medical literature, and referencing styles.
Dissertation Protocol: Steps to structure a research proposal, ethical considerations, and obtaining Institutional Review Board (IRB) approval.

Part 2: Biostatistics

Data Types & Management: Classification of data (qualitative vs. quantitative, nominal, ordinal, interval, ratio) and methods of data collection.
Descriptive Statistics: Measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation, variance).
Data Presentation: Constructing frequency distributions, tables, histograms, pie charts, and bar graphs.
Probability & Distributions: Concepts of probability, normal distribution curves, skewness, and kurtosis.
Sampling Techniques: Probability sampling (simple random, stratified, systematic, cluster) and non-probability sampling methods.

Part 3: Inferential Statistics & Hypothesis Testing

Parametric Tests: Application of Student’s t-test (paired and unpaired), Analysis of Variance (ANOVA), and Z-tests.
Non-Parametric Tests: Application of Chi-Square test, Mann-Whitney U test, and Wilcoxon signed-rank test.
Correlation & Regression: Assessing linear relationships using Pearson’s and Spearman’s correlation coefficients and simple linear regression models.
Errors in Testing: Understanding Type I (\(\alpha \)) and Type II (\(\beta \)) errors, statistical power, and interpreting \(p\)-values.