Experimental data analysis is important because:

• It helps us summarize large amounts of information into simple values
• It allows us to compare results from different trials or experiments
• It enables us to identify errors or inconsistencies in our measurements
• It helps us draw conclusions about whether our hypothesis is supported

1. Mean (Average)

The mean is the most common statistical value. It represents the central value of a set of measurements.

Formula: $\text{Mean} = \frac{\text{Sum of all measurements}}{\text{Number of measurements}}$

How to calculate:

1. Add all the measured values together
2. Divide the sum by the total number of measurements

2. Range

The range tells us how spread out the measurements are. It shows the difference between the highest and lowest values.

Formula: $\text{Range} = \text{Highest value} - \text{Lowest value}$

A small range means the measurements are close together (consistent results). A large range means the measurements are spread out (more variation).

3. Median

The median is the middle value when all measurements are arranged in order from smallest to largest.

How to find:

1. Arrange all values in ascending order
2. If there is an odd number of values, the middle one is the median
3. If there is an even number of values, add the two middle values and divide by 2

A Form 1 student measured the time for a pendulum to complete 10 swings five times. The results were:

Step 1: Calculate the mean $\text{Mean} = \frac{12.4 + 12.8 + 12.2 + 12.6 + 12.5}{5}$ $\text{Mean} = \frac{62.5}{5} = 12.5 \text{ seconds}$

Step 2: Calculate the range $\text{Range} = 12.8 - 12.2 = 0.6 \text{ seconds}$

Step 3: Find the median Arranged in order: 12.2, 12.4, 12.5, 12.6, 12.8 The middle value (3rd) = 12.5 seconds

Interpretation:

• The average time for 10 swings is 12.5 seconds
• The range of 0.6 seconds shows the measurements are consistent
• The mean and median are equal (12.5 s), indicating no extreme values

After statistical analysis, data should be presented clearly using:

• Tables: Organize raw data and calculated values
• Bar charts: Compare different categories
• Line graphs: Show trends over time or changing variables
• Histograms: Show distribution of continuous data

In Tanzania, statistical analysis is used when testing the performance of solar panels in physics projects at schools. For example, students at a school in Dar es Salaam might measure the voltage output of a solar panel at different times of day, calculate the mean voltage, and determine the range to see how much the power fluctuates. This helps them understand how reliable solar energy is for powering devices in their community, which is useful for planning electricity use in areas with limited grid connection.