T-Test Lab Report Examples for Students

Introduction to T-Test Lab Reports

A T-Test is a statistical test that helps determine if there is a significant difference between the means of two groups. It is widely used in various fields, including psychology, medicine, and education, to analyze data and draw conclusions. Below are three diverse examples of T-Test Lab Reports, showcasing different contexts and applications.

Example 1: Comparing Test Scores of Two Classes

In a high school setting, a teacher wants to assess whether two different teaching methods have an impact on student performance. One class uses traditional instruction, while the other employs a more interactive approach. The teacher collects test scores from both classes at the end of the semester.

The data collected is as follows:

• Class A (Traditional Instruction): [78, 85, 82, 90, 88]
• Class B (Interactive Instruction): [92, 95, 89, 94, 91]

After calculating the means, we find:

• Mean of Class A = 84.6
• Mean of Class B = 92.2

Using a T-Test, we determine the T-value and the p-value, which reveal whether the difference in means is statistically significant.

In this case, a T-Test indicates a p-value of 0.01, suggesting that the interactive method significantly improved test scores compared to the traditional method.

Notes:

• This example can be adjusted by varying the number of students or the teaching methods used.
• Ensure to check assumptions for T-Test, including normality and homogeneity of variance.

Example 2: Impact of a New Drug on Blood Pressure

A clinical trial is conducted to test the effectiveness of a new antihypertensive drug. Researchers want to compare the blood pressure of patients before and after taking the drug. The following data illustrates the systolic blood pressure readings (in mmHg) for 10 patients before and after treatment:

• Before Treatment: [150, 155, 145, 160, 158, 165, 162, 142, 148, 155]
• After Treatment: [140, 138, 145, 135, 130, 128, 132, 142, 136, 137]

Calculating the means gives:

• Mean Before Treatment = 153.3
• Mean After Treatment = 135.9

A paired T-Test is performed to evaluate the differences in blood pressure readings before and after treatment. The results yield a T-value and a p-value (e.g., p = 0.005), indicating a statistically significant decrease in blood pressure following the drug treatment.

Notes:

• This example can be expanded to include additional variables like age or gender.
• It is important to ensure that the data meets the assumptions of normality for the T-Test.

Example 3: Comparing Fitness Levels of Two Exercise Programs

A fitness instructor wants to compare the effectiveness of two different exercise programs on improving cardiovascular fitness. Group A participates in a high-intensity interval training (HIIT) program, while Group B engages in moderate steady-state cardio. The instructor measures the improvement in VO2 max (a measure of fitness) after 8 weeks:

• Group A VO2 Max Improvements (ml/kg/min): [3.5, 4.2, 3.8, 4.5, 4.0]
• Group B VO2 Max Improvements (ml/kg/min): [2.0, 2.5, 2.3, 2.8, 2.1]

The calculated means are:

• Mean Improvement for Group A = 4.0
• Mean Improvement for Group B = 2.5

After conducting an independent T-Test, the results show a p-value of 0.02, indicating that the HIIT program resulted in a significantly greater improvement in cardiovascular fitness compared to steady-state cardio.

Notes:

• Variations could include testing different fitness levels or longer duration programs.
• Consider collecting additional data points for a more robust analysis.