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Bio stats assignement, Study notes of Biochemistry

Bio stats assignement and detailed question

Typology: Study notes

2019/2020

Uploaded on 03/07/2025

ankush-singla
ankush-singla 🇺🇸

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Biostats Overview
Biostatistics is the application of statistical methods to
biological and medical research. It involves the design of
experiments, the collection and analysis of data, and the
interpretation of results. Here are some key concepts:
Types of Data 📊
Quantitative (Numerical) Data
oContinuous (e.g., height, weight, blood pressure)
oDiscrete (e.g., number of patients, mutations in a
gene)
Qualitative (Categorical) Data
oNominal (e.g., blood type, gender)
oOrdinal (e.g., cancer stage, pain severity:
mild/moderate/severe)
Descriptive Statistics: Descriptive statistics summarize
and describe the main features of a dataset. Examples
include measures of central tendency (mean, median,
mode) and measures of dispersion (range, standard
deviation).
Measures of Dispersion (How spread out is the data?) Range, Variance, Standard
Deviation, Interquartile Range (IQR)
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Biostats Overview Biostatistics is the application of statistical methods to biological and medical research. It involves the design of experiments, the collection and analysis of data, and the interpretation of results. Here are some key concepts: Types of Data 📊  Quantitative (Numerical) Data o Continuous (e.g., height, weight, blood pressure) o Discrete (e.g., number of patients, mutations in a gene)  Qualitative (Categorical) Data o Nominal (e.g., blood type, gender) o Ordinal (e.g., cancer stage, pain severity: mild/moderate/severe)  Descriptive Statistics: Descriptive statistics summarize and describe the main features of a dataset. Examples include measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation). Measures of Dispersion (How spread out is the data?) Range, Variance, Standard Deviation, Interquartile Range (IQR)

 Inferential Statistics: Used to draw conclusions from sample data to a larger population. a) Hypothesis Testing o Null Hypothesis (H₀): Assumes no effect/difference. o Alternative Hypothesis (H₁): Assumes there is an effect/difference. o P-value: Measures significance (typically, p < 0. means results are statistically significant). b) Common Statistical Tests o T-test: Compares means of two groups (e.g., drug vs. placebo). o Chi-Square Test: Tests for association between categorical variables (e.g., smoking & lung cancer). o ANOVA (Analysis of Variance): Compares means across three or more groups. o Regression Analysis: Examines relationships between variables (e.g., blood pressure vs. age).

Applications of Biostatistics

Clinical Trials – Evaluating new drugs, vaccines, and treatments. ✅ Epidemiology – Studying disease outbreaks and risk factors. ✅ Genetics – Identifying associations between genes and diseases. ✅ Public Health – Assessing health trends and policy impacts. ✅ Bioinformatics – Analyzing large-scale genomic and proteomic data.