Biostatistics and Variables Quiz

Descriptive and Comparative Statistics

• Biostatistics is the analysis of biological data for health and related fields
• Univariate analysis examines a single variable using frequency, proportions, and averages
• Bivariate analysis studies two variables for possible associations (like exposure and outcome), using rate ratios, odds ratios, and other comparative tests
• Multivariable analysis examines relationships among three or more variables, featuring tests like multiple regression

What is a Variable?

• A variable is any quantity that differs between entities, or within an entity over time
• Variables describe characteristics of people, places, things, or ideas
• Variables are measured during observations or experiments

Types of Variables

• Nominal variables have no inherent order or value. Examples: university programs, countries, or types of fruit

• Ordinal variables have an intrinsic order but no equal differences between levels. Examples: severity of pain, educational levels, or rating scales.

• Quantitative variables can be numerically measured.

  • Discrete quantitative variables have distinct values (like the number of pets a student owns).
  • Continuous quantitative variables can take any value within a range (like blood pressure or temperature)

Displaying Data

• Nominal or ordinal variables can be displayed using pie charts or bar graphs.
• Quantitative variables are displayed using histograms, which show the distribution of data values.

Measuring Variability

• Range is the difference between the highest and lowest values.
• Quartiles divide the data into four equal parts, with IQR (interquartile range) capturing the middle 50%.
• Variance measures the extent of data points' deviation from the mean.
• Standard deviation gives the typical deviation from the mean.
• Standard error represents the standard deviation of the sample mean.

Mean and SD in a Normal Distribution

• About 68% of data is within one standard deviation of the mean (μ ± σ)
• About 95% of data is within two standard deviations of the mean (μ ± 2σ)
• About 99.7% of data is within three standard deviations of the mean (μ ± 3σ)

Confidence Intervals

• Confidence intervals provide a range where the true population value is likely to fall.
• A 95% CI means there is a 95% chance that the interval contains the true population value

Comparative Statistics

• Comparative statistics compare variables in different groups, for example, comparing average age in exposed and unexposed groups
• Used to contrast characteristics between groups

Inferential Statistics

• Inferential statistics use sample data to draw conclusions about a larger population
• Key concepts include the null hypothesis (no difference) and alternative hypothesis (difference exists)
• Hypothesis testing determines if sample data supports the null hypothesis.

Parametric vs. Non-Parametric Tests

• Parametric tests assume particular data distributions (often normal).
• Non-parametric tests do not rely on specific distributions and are often preferred when data doesn't follow a normal distribution. Ordinal or ranked data is often analyzed via non-parametric testing.