This document provides an overview of statistical concepts related to analytical chemistry. It defines key terms like error, bias, accuracy, and precision. It discusses measures of central tendency, statistical process control charts, and various statistical tests. It provides examples of calculating Kjeldahl nitrogen and describes different types of control charts and statistical tests like t-tests, F-tests, linear regression, and analysis of variance. It lists several references for further information on statistics topics.

1. G.S.Nithej,
b.pharm,
s.p.s.p

2. Definition
Introduction
Statistics Importance
Analytical Chemistry

3. Error is the
collective noun for
any departure of
the result from the
true value.
Error
Bias
Accuracy
Precision
The “Trueness” or
the ‘’closeness’’ of
the analytical result
to the true value.
The consistent
deviation of analytical
results from the true
value caused by
systemic errors in a
procedure
The closeness with
which results of
replicate analysis of
a sample agree.

4. Mean
Standard deviation
Relative standard deviation
Confidence limits

5. I. Propagation of Random errors
II.Propagation of Systematic errors
1.Summation calculations
2.Multiplication calculations
• The calculation of Kjeldahl-Nitrogen may be as
follows

6. Types of Control charts
 X-bar Charts
 R- Charts
 P-Charts
 C-Charts

7. X-Charts

8. R-Charts

9. P-Charts and c-charts
 These are the control charts for attributes, which are not continuous variables but are things
that can be counted.
 A p-chart and c-chart considers the portion of a sample that is defective, where each item in
the sample is either defective or not.
 If we want to reduce the risk of falsely categorizing a good result as not being significant, we
can use a higher confidence level.
 To reduce the risk of falsely categorizing a non-significant result as significant, we can use a
lower confidence level.

10. Two-sided vs One-sided test
F-test for Precision
t-Test for bias
Linear correlation and regression
Analysis of variance (ANOVA)

11. Two-sided vs one sided test
 These tests for comparison, for instance between methods A and B, are based on the assumption
that there is no significant difference.
1. Are A and B different ?
(Two-sided test)
2. Is A higher or lower than B ?
(One-sided test).

12. t-Test
1.Students t – test
.
Student's t-test for comparison of two independent
standard deviations.
2.Paired t – test
The paired t-test for comparison of strongly

13. F-Test
The F-test (or Fisher's test) is a comparison of the spread of two sets of data to test if the sets belong
to the same population, in other words if the precisions are similar or dissimilar.
These are calculated by:
df1 = n1-1
df2 = n2-1

14. Linear correlation and regression
1. When the concentration range is so wide that the errors, both random and systematic, are not
independent which is the assumption for the t-tests.
2. When pairing is inappropriate for other reasons, notably a long time span between the two
analyses.

15. ANOVA
When results of laboratories or methods are compared where more than one
factor can be of influence and must be distinguished from random effects, then
ANOVA is a powerful statistical tool to be used.

16.  Statistics
 Need of statistics
 Measures of central tendancy and results
 Statistical process control analysis : control charts
 Statistical tests

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