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Biostatistics YEAR TOPICS 11A - Coggle Diagram
Biostatistics
YEAR TOPICS
11A
Definition and purpose
Definition:
Bioestadistics is a branch of statistics applied to the life sciences, such as biology and medicine. It deals with the collection, analysis, and interpretation of data to obtain accurate conclusions about biological and public health phenomena.
Purpose:
The purpose of bioestadistics is to design, analize, and interpret the clinical, observational and experimental studies in order to make decisions regarding the results obtained.
Importance in biology and medicine
Examples of biostatistical applications
Designing and analyzing clinical trials to test new treatments or drugs.
Deciding the aproppiate dose or treatment for the studied drug
Estimating disease prevalence and incidence in populations.
Evaluating the effectiveness of vaccines and public health interventions.
Allowing public intervention with them
clinical trials
Test new medicines to see if they work.
Compare results with people taking a placebo.
Use data to check if the medicine is safe and effective.
epidemology
Study how and why people get sick.
Find causes of diseases, like bacteria or lifestyle.
Use numbers to stop or prevent outbreaks.
Genetics & Genomics
Study how genes affect health and traits.
Find which genes cause certain diseases.
Use statistics to analyze DNA data.
Study of biological life and its aspect, allowing to understand the trends of a specific specie over a specific time lapse.
Biostatistics is a crucial tool in the fields of biology and medicine as it provides a means for researchers and healthcare providers to process data, derive proper conclusions and take scientifically backed decisions, which ultimately leads to better comprehending and managing of health problems.
Data and Variables
Types of data
Qualitative
Qualitative data refers to the non-numeric information that captures the descriptions and qualities of different phenomena
Cuantitative
Quantitative data refers to the information that can be measured, expressed, or counted in numeric values
Levels of measurement
Nominal
Ordianl
Interval
Ratio
Variable Roles
Dependant
AKA
Response variable
Independant
Data Collection
Populations and samples
Sampling methods
Random
Stratifies
Systematic
Experimental design basics
Data sets
Descriptive Statistics
Measures of central tendency
MEAN
The average
MEDIAN
MODE
Measures of dispersion
Range
Variance
Standard Deviation
Hypothesis
Ha
Alternative hypothesis
It is a statement that proposes that the is a significant relationship, effect or difference in a population, meaning that it is the opposite of a null hypothesis.
It represents the research claim a person is trying to porve stating that the observed data is not due to chance alone
H0
Null hypothesis
It is a statistical statement that proposes there is no significant relationship, difference or effect between two or more variables
It serves as a baseline assumption that researchers attempt to disprove through data analysis.
Hypothesis
ERRORS
Type I
H₀: Common errors include stating it incorrectly as having an effect, making it too vague, or failing to specify measurable variables.
Type II errors
H₁: Common errors include making it identical to H₀, being unclear about the expected direction of the effect, or not aligning it with the research question.
p-values and significance levels
:ballot_box_with_check: 0.05
:red_cross: < 0.05
PROBABILITY
Basic probability rules
Independent and dependent events
The Normal Distribution
Characteristics of the bell curve
Z-scores and standardization
Applications in biological data
Sampling and Sampling Distributions
Law of large numbers
Central limit theorem
Sampling error
Estimation and Confidence Intervals
Point estimates
Margin of error
Interpreting confidence intervals
SOFTWARE & PROGRAMING LENGUAJE
For statistical
analysis
EXCEL
Formulas
Pivot Tables
Graphs
R
R-STUDIO
PYTHON
Pandas