4 x 10 6 cm 3. Do Now: How may cm 3 in 4 m 3 ?. Experimental Errors & Uncertainty.

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Presentation transcript:

4 x 10 6 cm 3. Do Now: How may cm 3 in 4 m 3 ?

Experimental Errors & Uncertainty

Objectives Understand sources of uncertainty where they come from Define precision, accuracy. Understand sources of random & systematic errors. Minimize random & systematic errors.

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There is some uncertainty in every measurement. Why? Limits of measuring device. Experimental procedure/technique. Nature of measurement (too difficult) eg. Speed of light.

No Way! This scale is wrong! Experimental Errors There is always a difference between measured value & actual value. That is why we never use fractions to report a measurement!! Weight ≠ 220 1/2 lbs. Weight = lbs.

Random – measured values too high and too low. Measured values fall above & below actual. Caused by fluctuations in temperature, poor reflexes on stopwatch, poor vision, vibrations while measuring, variation in wire thickness. Two types of errors/uncertainty: Random & Systematic

True or False: Doing more experimental trials will reduce random error? True

Systematic Error – measured values fall consistently above or below actual values. Poor instrument calibration. Clock runs too fast or slow. Parallax error s.t. Balance is above or below zero. Zero offset error. Examples:

Will doing more trials help? Only with random error.

How can we correct systematic error? Instrument calibration Mathematical correction factor.

This graph shows which type of error? Why?

m mass volume

What are some ways to get zero offset errors? Calibration off Hooke’s law error Original reading (thumb on scale). Parallax error.

Which of the following are examples of experimental error? Misreading the scale on the pan balance. Incorrectly calculating a value by using the wrong data. Incorrectly transferring data from your notes into your lab report.

Accuracy & Precision should be reported with measurement Accuracy – how close a measurement is to the accepted value by %. Low systematic error. Measurement average near accepted value. Precision – the agreement among a number of measurements. Low random error – tight grouping.

What would the graphs look like?

Graph Accuracy Precision Accurate, not precise Accurate, precise Precise, not accurate inaccurate, imprecise

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