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Honesty and Integrity are the Foundation of Experiments
Fabrication/Falsification of Data is not an acceptable practice when conducting Scientific Experiments.
Verification Labs versus Inquiry-Based Labs
Verification Labs: "Established Result Labs" (These are generally informal labs in my Physics classes).
Definition: These activities replicate known experiments to confirm scientific results, such as measuring gravity (g).
Key Features:
Objective: To verify known scientific principles and relationships.
Procedure: Students follow established experimental methods to ensure consistency and reliability in their results.
Data Analysis: Students analyze their results using percentage error calculations to quantify the accuracy of their measurements. Additionally, they should identify sources of measurement error to explain discrepancies between their experimental results and accepted values. (Tables, charts, graphs and diagrams are expected for most of these labs)'
Conclusions/Outcomes: Students compare their results to the accepted values, considering equipment limitations. They should clearly articulate how their data supports or affirms established physics concepts. This process reinforces the importance of accuracy and helps students understand the impact of equipment limitations on experimental outcomes.
Experimental Activities: "Inquiry-Based Experiments" (These are generally Formal Group Labs in my Physics Classes).
Definition: In these activities, students design and conduct experiments to explore unknown outcomes, applying the scientific method.
Key Features:
Objective: To discover new insights or test hypotheses against predictions.
Procedure: Students engage in semi-structured to fully self-directed experimental design, allowing for creativity and critical thinking in their approach.
Data Analysis: Students assess data variability using standard deviation, Skewness, and Kurtosis. If there are only two meaningful sets of data, they should utilize percentage differences to compare results. (Tables, charts, histograms, graphs and diagrams are expected for most of these labs)'.
Conclusions: Students should clearly present their findings and reflect on what their data indicates about their hypotheses. They are encouraged to discuss whether their data leads them to accept or reject their hypothesis. Additionally, students should identify sources of measurement error and consider how these may have influenced their results.
Goal 1: Exposure to Data Logging: Generally considered direct measurements, providing real-time quantitative data from sensors.
Characteristics:
Direct Measurements: Data logging typically involves sensors that provide real-time data on physical phenomena. For example, a motion sensor measuring the velocity of a moving object provides direct measurements of that object's speed.
Quantitative Data: The data collected is usually quantitative, allowing for precise numerical analysis.
Real-Time Monitoring: Data logging systems can often display data in real time, enabling immediate analysis and feedback.
Goal 2: Exposure to Video Analysis: Considered indirect measurements, where data is extracted from recorded footage through post-processing.
Characteristics:
Indirect Measurements: Video analysis typically involves measuring parameters (like distance, speed, or acceleration) after the fact, by analyzing the recorded video. This is often done by tracking the movement of objects frame by frame.
Qualitative and Quantitative Data: While it can yield quantitative results (like calculating speed from distance and time), it often starts with qualitative observations before being converted into numerical data.
Post-Processing: Data extraction from video requires additional steps (e.g., using software to analyze frames), making it less immediate than data logging.
Goal 3: Exposure to Basic Statistics of Data
Mean, Median, Mode, Range, Skewness, Kurtosis, Quartile, Percentile, etc.
Goal 4: Exposure to Measurement Errors
Types of Measurement Errors
Systematic Errors:
Definition: Consistent, repeatable errors that occur due to a flaw in the measurement system or instrument.
Examples: Calibration errors (e.g., a scale not zeroed correctly)
Instrument bias (e.g., a thermometer consistently reading 2 degrees high).
Random Errors:
Definition: Errors that occur unpredictably and can vary from one measurement to another.
Examples: Fluctuations in readings due to environmental conditions (e.g., temperature, humidity).
Human error in reading instruments (e.g., parallax error).
Absolute Errors:
Definition: The difference between the measured value and the true value.
Example: If the true value is 10 cm and the measured value is 9.5 cm, the absolute error is 0.5 cm.
Relative Errors: (Percentage Errors)
Definition: The absolute error expressed as a fraction of the true value, often given as a percentage.
Example: If the true value is 10 cm and the measured value is 9.5 cm, the relative error is
0.5 cm / 10 cm × 100 = 5%.
Gross Errors:
Definition: Large mistakes that can occur due to human error or equipment malfunction, often resulting in outlier data points.
Examples: Incorrectly recording a measurement.
Equipment failure during measurement.
Instrumental Errors:
Definition: Errors inherent to the measurement instrument itself, often due to design flaws or limitations.
Examples: Response time lag in electronic sensors.
Non-linearity in analog measuring devices.
Environmental Errors:
Definition: Errors caused by external conditions affecting the measurement process.
Examples: Temperature fluctuations affecting readings.
Air pressure changes impacting certain measurements.
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