GraphPad Prism is a user-friendly commercial desktop application commonly used for the analysis of biological datasets.
This course provides both an introduction to core statistical concepts and common tests, as well as how to practically implement and visualise these in GraphPad Prism. The course goes through all common aspects of statistics, from power analysis and experimental design to summary statistics and the analysis of both quantitative and qualitative data. After attending this course you should have the theoretical knowledge of how to select and correctly apply a statistical test, and the knowledge of GraphPad Prism to put this into practice.
Pre-Course Requirements & Suggestions
This course makes no assumption about specific skills or knowledge
Course Content
Experimental Design
Proper consideration during the experimental design phase allows for better experiments and more reliable results. In this section, we look at key considerations that impact your analysis, including independent vs matched designs, technical and biological replicates, and choosing a statistical test before collecting any data.
Power analysis for sample size estimation
The concept of power is central in statistics and pivotal for the correct interpretation of p-values. Power analysis leads to sample size estimation, an important aspect of experimental design. This session introduces key statistical concepts within hypothesis testing, as well as how to estimate sample size using power calculations in GPower.
Descriptive statistics and data exploration
Proper understanding of descriptive statistics such as the mean, standard deviation, and standard error of the mean, allows us to better understand and summarise our data's behaviour, as well as represent it in an optimised way to others. Data exploration is a pivotal step in data analysis and a proper graphical exploration of your data is an essential step to ensure understanding and to identify any issues before any statistical analysis is carried out.
Analysis of quantitative data: Introduction
This section presents several key concepts, including statistical inference, the signal-to-noise ratio, and the assumptions for parametric tests. A stastistical test will always produce a p-value but the extent to which it is useful or valid depends on the data meeting several assuptions, so checking these is a necessary step during data analysis.
Analysis of quantitative data: Student's t-test
The t-test is what we use then when we want to know if there is a difference between 2 groups, providing our data meet parametric test assuptions. This section goes through how t-tests work, which one to use depending on your experimental design (indepent, paired, or one-sample), and the steps we should follow before running the test to ensure we are meeting the assumptions. Producing a p-value by running a statistic test is dead easy, doing it properly so that we can trust the results, not so much.
Analysis of quantitative data: ANOVA
Building on what is covered in the section on the Student's t-test, the one-way ANOVA is used when we want to compare more than 2 groups (e.g. one control and 2 treatments), while the two-way ANOVA is used when we have more than 2 predictors (e.g. genotype and treatment). We explore how these tests work and go through examples of how to run these within GraphPad Prism.
Analysis of quantitative data: Correlation and Linear Regression
In the previous sections, we covered the relationship between categorical (independent aka predictor) and quantitative (dependent aka outcome) variables. In this section, we look at the relationship between quantitative variables using correlation and linear regression.
Curve fitting
Whilst linear relationships are common, there are cases where non-linear relationships between quantitative variables need to be modelled. In this section we look at on example of this: dose response curves.
Analysis of quantitative data: Non-parametric statistics
In this module, we cover the main non-parametric tests. The classic parametric tests such as t-tests or correlation have non-parametric counterparts that we can use when our data fail to meet the parametric assumptions. We explore how these non-parametric tests work and the circumstances under which these would be a better choice for your analysis.
Analysing qualitative data
In the final section we move away from quantitative data to the analysis of qualitative data where we are looking at the counts of different categorical groups. As well as illustrating different ways to visualise this data we look at the chi-square and Fisher's exact test for the comparison of proportions across different categories in a dataset.