Per field of interest various aspects are being covered, such as:

**Nutrition:**

- Randomized placebo-controlled clinical trials
- Pilot studies
- Meta-analyses
- Surveys
- Etcetera

**Pharmaceutics and Bio-medics:**

- Phase I – Phase IV studies
- Natural compounds
- Drug evaluation
- Drug discovery
- Toxicity and tolerability
- Etcetera

**Techniques covered:**

- Design of experiment: calculation of a minimal amount of participants or runs in a multifactorial experiment. Main aim is to assess which factors affect the outcome in a certain parameter.
- Confounding: which (unexpected) factors affect the outcome in a certain parameter together with the treatment. Factors to be considered are typically: age, gender, BMI but also starting value of a certain parameter. As an example: the starting value of low-density lipoprotein (LDL) in participants of a trial will most certainly affect the impact by a particular intervention by a (natural) compound or drug in that trial.
- Survey analysis: population-based studies on certain conditions or diseases require specific statistical tools, such as weight factors (how many persons in a country/region are represented by a particular person participating in the study), finite population correction (based on the ratio of participating persons to the number in the overall population covered), estimation of biases, such as selection bias, etc.
- Mathematical modelling. This is performed not only to elucidate an underlying mechanism in acquired data but also to foretell a potential impact by a certain treatment in a study to be conducted or to estimate the effect in the future in its broadest sense.
- Power analysis: calculation of a minimal number of persons to be included in an experiment to optimize sensitivity (to be able to rightfully find the presence of a treatment effect) and specificity (to be able to rightfully find the absence of a treatment effect) on a statistically sound basis in any experiment.