Ch.4 + 5 - Statistics, Quality Assurance and Calibration MethodsWorksheetSee all chapters
All Chapters
Ch.1 - Chemical Measurements
Ch.2 - Tools of the Trade
Ch.3 - Experimental Error
Ch.4 + 5 - Statistics, Quality Assurance and Calibration Methods
Ch.6 - Chemical Equilibrium
Ch.7 - Activity and the Systematic Treatment of Equilibrium
Ch.8 - Monoprotic Acid-Base Equilibria
Ch.9 - Polyprotic Acid-Base Equilibria
Ch.10 - Acid-Base Titrations
Ch.11 - EDTA Titrations
Ch.12 - Advanced Topics in Equilibrium
Ch.13 - Fundamentals of Electrochemistry
Ch.14 - Electrodes and Potentiometry
Ch.15 - Redox Titrations
Ch.16 - Electroanalytical Techniques
Ch.17 - Fundamentals of Spectrophotometry
BONUS: Chemical Kinetics
Mean Evaluation
The Gaussian Distribution
Confidence Intervals
Hypothesis Testing (t-Test)
Analysis of Variance (f-Test)
Detection of Gross Errors

When dealing with data sets it becomes important to eliminate outliers in order to have the most accurate standard deviation. 

Grubbs Test vs. Q Test 

Concept #1: Both tests are useful in detecting a single outlier from a given data set. 


Example #1: Wishing to measure the amount of caffeine in a cup of coffee you pour ten cups. From the data provided perform a Q-test to determine if the outlier can be retained or disregarded.

Example #2: White blood cells are the defending cells of the human immune system and fight against infectious diseases. Provided below is the “normal” white blood cell counts for a healthy adult woman. Determine if the current white blood cell count is reasonable by Grubbs test.