1. Experimental Errors Kesalahan dalam pengukuran Sumber kesalahan
Rambatan kesalahan
Errors → noise in measured values

3. Can you hit the bull's-eye?
Three shooters with three arrows each to shoot.
How do they compare?
Both accurate and precise
Precise but not accurate
Neither accurate nor precise

4. Measure the diameter of the ball
Using a metric stick, determine the diameter of the ball provided.
Compare your results with another group.
Any problems with your measurement?

5. Can all errors be controlled?
What are some possible things that can be done to minimize errors?

6. Types of (experimental) Errors
Systematic Error
 Result of an experimental “mistake”
Sometimes called bias due to error in one direction- high or low
Penyebabnya diketahui (known cause)
Operator
Calibration of glassware, sensor, or instrument, etc.

7. Types of (experimental) Errors
Systematic Error
This error can be corrected/ controlled when causes of error are determined, i.e :
(a) calibrating all experimental tools and/
or instruments
(b) controlling skill of experimenter,
operator etc.
(c) Cleaning all glassware, bottles, etc
before doing experiments
(d) etc… ?????

8. Types of (experimental) Errors
Systematic Error
Typically produce constant or proportional nature (slowly varying bias)
y = ax + b
Proportional error influences the slope.
Constant error influences the intercept.

9. Types of (experimental) Errors
Random error
Unpredictable, non-deterministic
Unbiased → equal probability of increasing or decreasing measured value
Result of
Limitations of measuring tool
Random processes within system
Environmental effect (?), etc
Typically cannot be controlled
Use statistical tools to characterize and quantify
Multiple trials help to minimize

10. Example: Sources of Error sampling preparation analysis Representative
sample
homogeneous
vs.
heterogeneous
preparation
Loss
Contamination
(unwanted addition)
analysis
Measurement
of Analyte
Calibration of
Instrument or
Standard
solutions

11. Example: Quantization → Random error1 13 1 14

12. Quantization error Timer resolution → quantization error
Repeated measurements
X ± Δ
Completely unpredictable

13. A Model of Errors
Error
Measured value
Probability -E
x – E +E
x + E

14. A Model of Errors Error 1 Error 2 Measured value Probability -E x – 2E+E x
x + 2E

15. A Model of Errors

16. Systematic errors → accuracy
How close mean of measured values is to true value
Random errors → precision
Repeatability/reproducibility of measurements
Characteristics of tools → resolution
Smallest increment between measured values

17. Most accurate and precise
Graphical methods
Scatter plots
Most accurate and precise
Systematic error?
Worst precision

18. Two students analyzing two different CaCO3 antacid tablets
True value
Student 1
Student 2
Label value
500 mg
750 mg
Mean
463 mg
761 mg
Std. dev.
20 mg
28 mg
Which student has the more accurate results?
Which student has the greater precision?

19. How are we going to address these questions?
quantity
Student 1
Student 2
% Relative standard deviation  asses the precision
%Error  asses the accuracy

20. calibration marks + one more place
Always remember to…
Make all measurements carefully and check your results or readings a second time.
Read all devices to as many places as possible (significant figures):
calibration marks + one more place
A buret, which is calibrated to 0.1 mL, can be read to 0.01 mL.
A thermometer marked every degree can be read to 0.1 degree

21. Kandungan rhodamin dalam sampel Metode Spektrofotometri
Metode ESI
Metode Spektrofotometri
Ulangan 1 2 3
Saos A
0.11
0.25
0.15
0.10
0.17
Saos B
0.22
0.34
0.32
0.38
Berapakah konsentrasi rhodamin pada saos A dan B yang terukur dengan metode ESI dan spektrofotometri?
Berapakah ketelitian dan ketepatan pengukuran rhodamin dengan kedua metode jika konsentrasi rhodamin sebenarnya dalam saos A 0.18 ppm dan saos B 0.24 ppm?
Apa saja sumber systematic dan random errors dalam pengukuran tersebut?