Unit 22: Biostatistics — Short Questions

Biostatistics provides the framework for designing valid, unbiased clinical studies and for analyzing the resulting data reliably. It allows biostatisticians to evaluate the safety and effectiveness of new drugs, treatments and medical devices through properly designed trials and appropriate statistical tests. This enables healthcare professionals to make evidence-based decisions, improving patient care and advancing medical knowledge, rather than relying on guesswork or testing entire populations, which is impractical and costly.

Descriptive statistics summarize and describe the characteristics of a dataset itself, using measures such as mean, median, mode and standard deviation. Inferential statistics go further — using techniques like hypothesis testing, regression analysis and modelling — to draw conclusions about a larger population based on sample data.

Quartiles are special percentiles that divide a dataset into four equal parts, whereas percentiles divide it into 100 equal parts. The first quartile (Q1) is the 25th percentile, the second quartile (Q2, the median) is the 50th percentile, and the third quartile (Q3) is the 75th percentile. In other words, every quartile is a percentile, but percentiles give a finer (1%) division of the data than quartiles (25% divisions).

The independent variable is the factor that the experimenter deliberately changes or manipulates, while the dependent variable is the factor that is measured or observed. The dependent variable changes in response to changes made in the independent variable, so testing this cause-and-effect relationship between the two is the basis of accepting or rejecting the experimental hypothesis.

Step 1 Data is already arranged in ascending order: 110, 115, 118, 120, 122, 125.
Step 2: n = 6 (even number of values).
Step 3: Since n is even, median = average of the two middle values (3rd and 4th positions) = (118 + 120)/2.
Median = 238/2 = 119 cm.

A legend is a key or guide on a chart, graph, or map that explains the meaning of the symbols, colours, patterns, or labels used in it. It helps the viewer understand what each visual element represents, making the data easier to interpret and analyse — for example, identifying which colour in a bar chart corresponds to which category.

Error bars are graphical elements placed on bar charts and other plots to visually represent the variability or uncertainty in data. They show how much individual data points typically deviate from the central tendency (e.g., the mean) represented by the bar, and are commonly drawn as either range error bars (minimum to maximum) or standard deviation error bars (mean ± SD).