Experimental Planning and Biostatistics in Biotechnology

The course is organised in two units:
Unit 1 - Experimental planning
Unit 2 - Biostatistics

Student can choose to attend this course (both teaching units) at the first or at the second year (II semester).

As the skills acquired with this teaching are useful for the experimental internship activities, students are warmly recommended to attend the course in the first year.

Lesson attendance is strongly encouraged.

The specific program of each unit is reported below.


EXPERIMENTAL PLANNING
Analytical and preparative methodologies. Differences between analytical and preparative modes of a methodology and their role in an experimental approach. Experimental approach as a proper and sequential combination of different methodologies. Identification of the experimental aim and the effect on the plan. The search of most informative experimental aims. Qualitative and quantitative data. The importance of control samples in the application of a methodology. Bottom-up experimental planning. The experimental aim as starting point in building up an experimental approach. Choice of the methodologies and the assessment of the critic points. The importance of the sample material. Experimental documentation. The tracking of the experiment data. Image densitometry analysis to generate qualitative and semi-quantitative data from electrophoretic and blotting analyses. Simple data elaboration and presentation (e.g. reiterated calculations, standard curve, inhibition curve). The above-mentioned points will be limited to the experimental approaches based on biomolecular methodologies (e.g.: chromatography methods, enzyme methods, immunochemical assays, colorimetric assays, inhibition assays, electrophoretic methods, blotting analyses, DNA manipulation methods, etc.), and most part of the program will be exposed with the help of real case studies.

BIOSTATISTICS
Biostatistics will be presented with a practical approach emphasizing the rationale of statistical theory and methods rather than mathematical proofs and formalisms. Each theoretical lecture will be integrated with practical application and exercises using simple spreadsheet to analyse experimental data. Introduction: descriptive and inferential statistics. Samples and populations. Types of variables: qualitative, quantitative. Precision and accuracy.Data visualization: Absolute, relative and cumulative frequency tables. Representing the frequency distributions, diagrams and histograms, percentiles and quantiles, contingency tables. Scatterplot, line chart, maps. Describing Data: Measures of location and dispersion, geometric and arithmetic mean, median, mode, interquartile range, range of variation, deviation, variance, standard deviation, coefficient of variation.Estimate with uncertainty: sampling distribution, standard error and confidence interval. Probability: basic rules, Venn diagrams, probability trees. Sum the odds. Independence and the product rule. Conditional probability. Hypothesis testing: null and alternative hypotheses and statistical significance, P-value. Hypothesis testing and confidence intervals. Error of first and second species. Analysis of proportions: binomial distribution. Estimate of the proportions: confidence interval and standard error of a proportion. Chi-square test and the goodness of fit. Poisson distribution. Contingency tables for the analysis of categorical variables and chi-square test for the analysis of contingency tables. Normal distribution, formula, assumptions and properties. The central limit theorem. Normal approximation for binomial distribution. Inference in a population with a normal distribution: t-distribution, assumptions and properties. t-test for one sample. Comparison between two means, paired comparison between means, comparing the means of two samples. Comparisons between means of multiple groups: analysis of variance. Measurements of relationship between variables: covariance, correlation and linear regression. Least squares. Linear regression.