Lesson Notes By Weeks and Term v4 - SHS 3
RESEARCH AND DESIGN IN BIOMEDICAL SCIENCE
Download the Lessonotes Mobile Ghana app for faster lesson access on Android and iPhone.
RESEARCH AND DESIGN IN BIOMEDICAL SCIENCE
Download the Lessonotes Mobile Ghana app for faster lesson access on Android and iPhone.
Subject: Biomedical Science
Class: SHS 3
Term: 2nd Term
Week: 6
Grade code: 2.4.1.LI.2
Strand code: 4
Sub-strand code: 1
Content standard code: 2.4.1.CS.1
Indicator code: 2.4.1.LI.2
Theme: BIOMEDICAL INNOVATION
Subtheme: RESEARCH AND DESIGN IN BIOMEDICAL SCIENCE
This page supports the lesson note with a companion video and a short classroom-ready summary.
For class groups and homework, share this lesson page so learners also get the summary, objectives, and full lesson context.
Welcome, future biomedical scientists! In our journey to understand and improve health in Ghana, we don't just guess. We gather information (data) and analyse it carefully. Imagine the Ghana Health Service trying to determine if a new vaccine is effective in preventing malaria in the Volta Region, or a researcher at the Noguchi Memorial Institute testing a new herbal remedy for high blood pressure. They rely on statistics to make sense of their results. Statistics is the language we use to turn raw numbers from an experiment into meaningful conclusions.
This section breaks down the fundamental statistical tools you need. We will use a running example to make it practical.
Scenario: A researcher at Korle Bu Teaching Hospital is testing a new drug, "Cardio-Calm," to see if it reduces systolic blood pressure in patients. They select a sample of 10 patients and record their blood pressure reduction (in mmHg) after one week of treatment. Data Set (Blood Pressure Reduction in mmHg): `10, 12, 15, 15, 17, 18, 20, 22, 25, 36` Part A: The Building Blocks of a Study Variables: In any experiment, we are looking at the relationship between different factors, or variables. Independent Variable (IV): This is the variable that the researcher changes or controls. It is the "cause" in a cause-and-effect relationship. Think of it as the treatment or intervention. *In our Korle Bu scenario:* The Independent Variable is the administration of the Cardio-Calm drug (e.g., dosage, or simply whether the patient received it or a placebo). Dependent Variable (DV): This is the variable that is measured or observed. It is the "effect" that is expected to change in response to the independent variable. Think of it as the outcome or result. *In our Korle Bu scenario:* The Dependent Variable is the reduction in systolic blood pressure (in mmHg). The researcher measures this to see the effect of the drug. Sample Size: The sample is the small group of individuals selected from a larger population (e.g., the 10 patients are a sample of all hypertensive patients in Accra). Sample size (n) is the number of individuals in the sample. Why does it matter? A larger sample size is generally better because it is more likely to be representative of the entire population. *Example:* If we want to know the average height of SHS3 students in Ghana, is it better to measure 10 students from one school or 1000 students from schools across all 16 regions? The 1000 students will give us a much more accurate average for the whole country. A small sample can be easily skewed by one or two unusual results (outliers). Part B: Measures of Central Tendency (Describing the "Centre" of the Data)
These statistics tell us where the middle or typical value in our data set is. Mean (Average): The most common measure. It is the sum of all values divided by the number of values. Formula: Mean (μ or x̄) = Σx / n (where Σ means "sum of", x is each value, and n is the sample size). Calculation for our scenario: `Sum (Σx) = 10 + 12 + 15 + 15 + 17 + 18 + 20 + 22 + 25 + 36 = 190` `Sample size (n) = 10` `Mean = 190 / 10 = 19 mmHg` Interpretation: The average blood pressure reduction for this sample was 19 mmHg. Median (Middle Value): The middle value when the data is arranged in ascending order. It is less affected by extreme values (outliers) than the mean. Steps: Arrange data in order: `10, 12, 15, 15, 17, 18, 20, 22, 25, 36` Find the middle position. If n is odd, it's one value. If n is even (like ours), it's the average of the two middle values. Our middle values are the 5th (17) and 6th (18) numbers. `Median = (17 + 18) / 2 = 17.5 mmHg` Interpretation: Half of the patients had a blood pressure reduction of 17.5 mmHg or less, and half had more. Notice the single high value (36) pulled the mean (19) up, but the median was not affected as much. Mode (Most Frequent Value): The value that appears most often in the data set. Steps: Look at the ordered data: `10, 12, 15, 15, 17, 18, 20, 22, 25, 36` `Mode = 15 mmHg` Interpretation: The most common reduction observed was 15 mmHg. A dataset can have one mode (unimodal), two modes (bimodal), or no mode if all values appear only once. Part C: Measures of Variability (Describing the "Spread" of the Data)
These statistics tell us how spread out or clustered together our data points are. Range: The simplest measure of spread. It's the difference between the highest and lowest values. Formula: Range = Maximum Value - Minimum Value Calculation for our scenario: `Range = 36 - 10 = 26 mmHg` Interpretation: The blood pressure reductions varied by as much as 26 mmHg across the patients. Variance (s²): A more sophisticated measure. It represents the average of the squared differences from the Mean. A larger variance means the data is more spread out. Formula for a sample: s² = Σ(x - x̄)² / (n - 1) Step-by-step Calculation: