Statistical Analysis of Experimental Data

STATISTICAL ANALYSIS OF EXPERIMENTAL DATA

Statistics| Statistical Treatment| Data Analysis in Life Sciences	Calibration and Outliers - Statistical Analysis	Detection of a Single Outlier|Statistical Analysis|Quantitative Data
Detection of Outliers in Analytical Data – The Grubb’s Test	Dixon's Q-test calculator - Detection of a single outlier	One-Sample T-Test in Chemical Analysis – Statistical Treatment of Analytical Data
Statistics| Analysis of Data – Confidence intervals	Statistics – Frequency Distributions, Normal Distribution, z-scores	Testing for Normality of Distribution (the Kolmogorov-Smirnov test)
Comparing several Group Means by ANOVA using SPSS

 

The major objectives of the application of statistics to Science and to chemical analysis are to determine the best value of a series of analytical results obtained with a particular sample and to give some indication of the reliability of the analysis.

As scientists, we are interested in finding results that apply to an entire population of people/ or material/ or chemical substance.

There is no doubt that it would be extremely expensive and impractical to analyze the entire population to draw conclusions about certain variables.
Therefore, in most cases we collect data from a small subset of the population (known as sample) and use these data to infer things about the population as a whole. This small sample’s  statistical measurements is the “model” for the entire population.

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References

1. D. Harvey,  “Modern Analytical Chemistry”, McGraw-Hill Companies Inc., (2000)
2. L.R. Ellison, V.J. Barwick, T.J.D. Farrant, “Practical Statistics for the Analytical Scientist”, 2nd Edition, Royal Society of Chemistry, (2009) 
3. D.B. Hibbert, J.J. Gooding, "Data Analysis for Chemistry", Oxford Univ. Press, (2005)
4. J.C. Miller, J.N. Miller, "Statistics for Analytical Chemistry",3 Sub edition, Ellis Horwood Ltd, (1993)

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