Biostatistics for Biotechnology: Data, Probability & Analysis
🔵 Unit I — Introduction to Biostatistics
Biostatistics: Definition and Role
Biostatistics is a specialized branch of statistics concerned with the application of statistical principles and methods to biological, medical, and life-sciences data. In modern biological sciences, experiments and observations generate large volumes of data that cannot be interpreted accurately without proper statistical tools. Biostatistics provides a scientific framework to plan experiments, analyze experimental
Read MoreEssential Statistical Methods for Data Analysis
Understanding Statistical Dispersion
Dispersion is the extent to which data values in a dataset are spread out or scattered around a central value, such as the mean or median. It quantifies the variability or consistency within the data, complementing measures of central tendency (which describe the center of the data). A high dispersion indicates widely scattered data, while low dispersion suggests data points clustered closely together.
Measures of dispersion are essential for understanding data
Read MoreStatistical Test Interpretation and SPSS Decision Rules
Statistical Significance: The Main Rule
The decision rule for hypothesis testing is based on the p-value:
- p < 0.05: Significant → Reject H0 (Null Hypothesis)
- p ≥ 0.05: Not significant → Fail to reject H0
Choosing the Appropriate Test:
- 1 group vs known value → One-Sample T-Test
- 2 groups (different people) → Independent Samples T-Test
- 2 groups (same people before/after) → Paired Samples T-Test
- 3+ groups → ANOVA (+ Tukey Post-Hoc if significant)
- Numeric ↔ Numeric relationship → Correlation
- Predict
Mastering APA Statistical Reporting and Research Design Principles
APA Statistical Reporting Examples
This section demonstrates how to report key statistical findings and sample characteristics using proper APA format.
Sample Size and Demographics
- Total Participants: The total sample size was reported as N = 98. (Note: The capitalized N represents the full sample; a lowercase n represents a subset.)
- South Asian Identification: 9.20% of the sample identified as South Asian.
- Male Participants: 50.00% of the sample was identified as men.
- Non-Respondents for Age: Six participants
Essential Statistical Concepts: Data Analysis and Modeling
Statistics: techniques (collecting,organizing,analysing,interpreting data)
Data may be:
quantitative (values expressed numerically) qualitative: (characteristics being tabulated). Descriptive statistics
: techniques summarize, describe numerical data= easier interpretation – can be graphical/involve computational analysis. Inferential statistics: techniques about decisions about statistical population/process are made based only on a sample being observed – use of probability concepts. VARIABLES: