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Mean, median, and mode are all measures of central tendency and represent a “typical” data point from your dataset.

The Z-score, or standard score, tells us how far a value is from the population mean. We can use Z-scores to calculate confidence intervals, compare scores measured on different scales, and perform a Z-test.

The two main approaches to data analysis can be categorized into descriptive and inferential statistics. While descriptive analysis helps you get an overview of your dataset and describe its characteristics, inferential statistics aim at testing hypotheses and drawing conclusions from a sample to a larger population.

Prepare an elevator pitch on yourself and your research to make sure you never miss good opportunities to present your work. Below you’ll find a pitch recipe to get started.

The p-value is one of the most reported statistical measures within quantitative research, hence it’s crucial to know how to interpret it correctly.

A confidence interval is a way of indicating the margin of error, or the degree of uncertainty or certainty, of a measurement or a calculation. It gives you an indication of how precisely the result of your calculation represents the “true value” in the population.

Survival analysis, also known as time-to-event analysis or failure-time analysis, can be relevant for any research question where time is of interest.

Missing data may severely impact the statistical analyses and the validity of the data in a study. Mechanisms for missing data should be identified as early as possible so that measures to prevent or compensate for missing data can be implemented.

Missing data may undermine the ability to draw valid conclusions from any clinical trial, patient registry or research project. There are numerous causes that data may be lost, unavailable or not observed. If substantial, missing values can reduce the statistical power and introduce biases in a dataset regardless of study design. Although it may be impossible to completely avoid missing data, it is critical to take steps in the design process to ensure that the risk for missing data is minimized. Also, a pre-specified plan should be established for how to handle missing data throughout the operational phase and in the analyses.

The aim of all clinical research studies is to derive valid conclusions that are relevant in the real world. The study design is a tool to answer the questions in the most effective manner.

Association. Correlation. Causation. Do you know the difference?

Parametric statistical tests rely on assumptions about the shape of the distribution and the parameters (i.e. mean and standard deviation), and most rely on an assumption of an approximately normal distribution. Nonparametric statistical tests rely on no or few assumptions about the shape or the parameters of the population distribution from which the sample was drawn. If the data are indeed normal, a nonparametric test will generally have less power for the same sample size compared to the corresponding parametric test.