University:
University of the People
Course:
HS 4510 | Biostatistics
Academic year:

2024
Views:

275

Pages:

3
Author:

amrinderjeet kaur

UNIT 8 LEARNING JOURNAL Throughout the HS 4510 Biostatistics course, I gained a fundamental understanding of statistical methods applied to biological and health-related data. Key concepts included: 1. Descriptive statistics taught me how to use measures of variability (range, variance, and standard deviation) and central tendency (mean, median, and mode) to summarize and interpret data. 2. Probability and Distributions: Analyzing biological data required an understanding of probability distributions, such as the normal, binomial, and Poisson distributions. 3. Inferential Statistics: This covered the difference between sample statistics and population parameters, confidence intervals, and hypothesis testing. 4. Study Design and Sampling: In order to lessen bias, I studied random sampling techniques and investigated a variety of study types, such as observational and experimental studies. 5. Comparing Means and Variability: While comparing datasets in healthcare studies, I learned about t-tests, ANOVA, and chi-square tests. 6. Correlation and Regression: It was also helpful to learn how to use regression models and correlation coefficients to examine links between variables. Challenges Faced Knowing How Probability Distributions Work At first, it was challenging to understand the various probability distributions and how they were used. To completely grasp their importance in biostatistics, examples and practice problems had to be presented repeatedly. Complexity of Hypothesis Testing: It was difficult to formulate null and alternative hypotheses, choose the best statistical test, and interpret p-values. It took some effort to comprehend the logic behind Type I/II mistakes and statistical significance. Using Statistical Software: Although the course did not require any particular software, it would have improved my learning experience to be able to interpret statistical results using programs like Excel or SPSS. Interpreting Regression Analysis: Although correlation was easy to understand, multiple regression analysis and coefficient interpretation took more work. Questions Regarding the Course Beyond theoretical courses, how may biostatistical methods be used in practical public health research? Are there any more materials or hands-on activities available to help me better comprehend the statistical tools used in biostatistics? How may one avoid frequent mistakes when analyzing statistical results from clinical studies? In contemporary healthcare analytics, how do machine learning methods and biostatistics interact? All things considered, this course gave students a strong foundation in biostatistics and how it is used in the health sciences. Even if it took more work to properly understand some of the topics, the knowledge acquired will be helpful for future academic and professional pursuits in public health research and biomedical engineering. REFERENCES: ● Illowsky, B. and Dean, S. (2017). Introductory Statistics. OpenStax Rice University. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).