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Statistics for Scientists Introduction

This one day introductory course to the fundamental of statistics, relevant to all professionals in the analytical sciences, life sciences and related fields.


  • Course delivered face-to-face in one day or online in two half-days
  • We limit numbers to 20 per course so that each delegate gets the opportunity to ask questions and fully participate in tutorial exercises
  • When delivered on-site we can design the course material to suit your specific training needs


Course Overview | Download course details »

This course comprehensively covers the basic concepts and methods of statistics with applications in the field of analytical sciences. During this course, we will demonstrate methods of exploring, organising, and presenting data, and introduces the fundamentals of probability.
We will:
- explore the foundations of statistical inference, including the concepts of parameters and estimates
- explore the use of the statistical measures, confidence intervals, and hypothesis tests.

Who is this course for

The aim of the course is to foster an interest in applied statistics and equip scientists and project managers to confidently apply statistical techniques to investigate and improve data quality.

Previous knowledge

Good knowledge of chromatography and statistical analysis is advantageous.

What you will learn

  • Quantifying Errors
  • Normal (Gaussian) Distribution
  • Significance Tests
  • Statistical Measures
  • Confidence Limits
  • Analysis of Variance


Course Outline


  • Analytical problems
  • Errors in quantitative analysis
  • Random and systematic errors
  • Accuracy, repeatability, reproducibility
  • Standard reference materials

Statistical Measures

  • Mean and standard deviation
  • Variance and coefficient of variation

Normal (Gaussian) Distribution

  • Sampling distributions
  • Confidence limits
  • Significant figures
  • Propagation of errors

Significance Tests

  • Null hypothesis, type I and type II errors
  • Comparison of and
  • Comparison of and
  • t-tests
  • F-tests
  • One-sided and two-sided tests
  • Outliers
  • Analysis of variance (ANOVA)
  • Fixed effects and random effects
  • Comparison of several means

Regression and Correlation

  • Calibration graphs
  • Correlation coefficient
  • Regression of y on x
  • Errors in the regression line
  • Calculation of a concentration
  • Limit of detection
  • Standard additions
  • Outliers in regression
  • Weighted regression lines

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