🫁Intro to Biostatistics Unit 1 – Descriptive Statistics

Key Concepts and Definitions

• Descriptive statistics involves methods for organizing, summarizing, and presenting data in a meaningful way
• Population refers to the entire group of individuals, objects, or events of interest while a sample is a subset of the population used for analysis
• Parameters are numerical values that describe characteristics of a population (usually unknown) while statistics are numerical values calculated from sample data to estimate population parameters
• Qualitative (categorical) data consists of non-numerical attributes or categories (gender, blood type) while quantitative (numerical) data represents measurements or counts (height, age)
• Discrete quantitative data can only take on specific values, often integers (number of siblings) while continuous quantitative data can take on any value within a range (weight, temperature)
• Univariate analysis examines one variable at a time while bivariate analysis explores relationships between two variables
• Frequency distributions organize and summarize data by counting the occurrences of each value or category
  • Relative frequency is the proportion of observations in each category, calculated by dividing the frequency by the total number of observations

Types of Data and Variables

• Nominal data consists of categories with no inherent order or ranking (race, religion)
  • Dichotomous (binary) variables have only two possible categories (alive/dead, yes/no)
• Ordinal data has categories with a natural order or ranking, but differences between categories are not necessarily equal (socioeconomic status, pain severity)
• Interval data has ordered categories with equal intervals between values, but no true zero point (temperature in Celsius or Fahrenheit)
• Ratio data has ordered categories, equal intervals, and a true zero point representing the absence of the variable (height, weight, income)
• Independent variables (predictors) are manipulated or controlled to observe their effect on dependent variables (outcomes)
• Confounding variables are related to both the independent and dependent variables, potentially influencing the observed relationship between them (age, smoking status)
• Effect modifiers (interaction terms) change the magnitude or direction of the relationship between an independent and dependent variable at different levels of the modifier (gender, genetic factors)

Measures of Central Tendency

• Mean (arithmetic average) is the sum of all values divided by the number of observations, sensitive to extreme values (outliers)
• Median is the middle value when data is ordered from lowest to highest, robust to outliers and suitable for skewed distributions
  • For an even number of observations, the median is the average of the two middle values
• Mode is the most frequently occurring value in a dataset, useful for describing categorical or discrete data
• Geometric mean is calculated by multiplying all values and taking the nth root (where n is the number of observations), used for positively skewed data or ratios
• Harmonic mean is the reciprocal of the arithmetic mean of reciprocals, used for rates or ratios (average speed, drug clearance rates)
• Weighted mean accounts for the relative importance of each value by assigning weights, used when some observations are more influential than others

Measures of Variability

• Range is the difference between the maximum and minimum values, providing a simple measure of dispersion
• Interquartile range (IQR) is the difference between the 75th and 25th percentiles (Q3 - Q1), a robust measure of dispersion less sensitive to outliers
• Variance is the average squared deviation from the mean, quantifying how far observations are from the center
  • Sample variance (s²) has a denominator of n-1 to account for the loss of one degree of freedom when estimating the population variance
• Standard deviation is the square root of the variance, expressing dispersion in the same units as the original data
• Coefficient of variation (CV) is the ratio of the standard deviation to the mean, allowing comparison of variability across variables with different units or scales
• Standard error of the mean (SEM) estimates the variability of the sample mean, calculated as the standard deviation divided by the square root of the sample size
  • Smaller SEM indicates more precise estimates of the population mean

Data Visualization Techniques

• Histograms display the frequency distribution of continuous data using adjacent rectangles, with the area of each rectangle proportional to the frequency of observations in that bin
• Bar charts compare frequencies or proportions of categorical data using separate rectangles, with the height of each bar representing the frequency or proportion
• Pie charts show the relative frequencies of categorical data as slices of a circle, with the area of each slice proportional to the frequency or proportion
• Box plots (box-and-whisker plots) summarize the distribution of continuous data using five summary statistics (minimum, Q1, median, Q3, maximum) and identify outliers
• Scatter plots display the relationship between two continuous variables, with each point representing an observation and its coordinates corresponding to the values of the variables
• Line graphs connect data points in order, often used to show trends or changes over time
• Heat maps use color intensity to represent the magnitude of a variable across two dimensions (e.g., gene expression levels across samples and conditions)

Interpreting Descriptive Statistics

• Assess the shape of the distribution (symmetric, skewed, bimodal) to select appropriate summary statistics and statistical tests
  • Skewed distributions have a long tail on one side and may require non-parametric methods or data transformations
• Consider the presence of outliers, which can greatly influence the mean and standard deviation but have less impact on the median and IQR
• Use measures of central tendency to describe the typical or representative value in a dataset
  • The mean is often used for normally distributed data, while the median is preferred for skewed distributions or when outliers are present
• Employ measures of variability to quantify the spread or dispersion of the data, providing context for the central tendency measures
  • A small standard deviation indicates data points are clustered closely around the mean, while a large standard deviation suggests greater variability
• Interpret the standard error of the mean as a measure of the precision of the sample mean estimate, with smaller values indicating more reliable estimates
• Utilize data visualization techniques to identify patterns, trends, and relationships between variables, as well as to communicate findings effectively

Applications in Biomedical Research

• Summarizing patient characteristics (age, BMI, blood pressure) and comparing across treatment groups in clinical trials
• Describing the distribution of biomarkers (glucose levels, tumor size) and establishing reference ranges for diagnostic purposes
• Analyzing epidemiological data (prevalence, incidence rates) to understand disease burden and risk factors in populations
• Exploring relationships between variables (dose-response curves, genotype-phenotype associations) to generate hypotheses and guide further research
• Monitoring quality control metrics (assay variability, batch effects) to ensure reliability and reproducibility of experimental results
• Communicating research findings to diverse audiences (clinicians, policymakers, the public) using clear and informative data visualizations

Common Pitfalls and Misconceptions

• Overinterpreting small differences in summary statistics without considering the variability and uncertainty in the data
• Failing to recognize the limitations of summary statistics, such as the sensitivity of the mean to extreme values or the inability of the median to capture the full range of the data
• Misusing or misinterpreting the standard error of the mean as a measure of the variability of individual observations rather than the precision of the sample mean estimate
• Neglecting to assess the assumptions underlying certain statistical methods, such as the normality assumption for parametric tests
• Confusing statistical significance with practical or clinical significance, as small differences may be statistically significant in large samples but not meaningful in practice
• Overrelying on p-values and neglecting effect sizes and confidence intervals, which provide more informative measures of the magnitude and precision of the observed effects
• Failing to account for multiple comparisons when conducting numerous hypothesis tests, increasing the likelihood of Type I errors (false positives)
• Inappropriately extrapolating findings from a sample to a population without considering the representativeness of the sample and potential sources of bias