Introduction to statistics
- Sample size (one observation → n=1)
- Comparison (control group, for example no treatment group or placebo)
- Confounding factor (factor that has an influence on the outcome of the experiment)
- Sampling should be representative for the whole population.
- Ethics
- Blinding (not knowing which organisms get which treatment, organisms not knowing
that they get treatment)
- Effect size (how big is the effect)
Statistical significance
H0 → hypothesis of no effect.
How well do the data fit the null hypothesis?
When the null hypothesis is rejected→ the observation is statistically significant.
P value tells something about the data. It shows if the hypothesis is true or not true.
How to take random samples?
Random samples: means that every subject in the population has the same chance of being
selected in the sample.
How large should the sample be?
How to check and validate the data? (Typos and other errors when entering data)
Experimental design
- Sample size (one observation → n=1)
- Comparison (control group, for example no treatment group or placebo)
- Confounding factor (factor that has an influence on the outcome of the experiment)
- Sampling should be representative for the whole population.
- Ethics
- Blinding (not knowing which organisms get which treatment, organisms not knowing
that they get treatment)
- Effect size (how big is the effect)
Statistical significance
H0 → hypothesis of no effect.
How well do the data fit the null hypothesis?
When the null hypothesis is rejected→ the observation is statistically significant.
P value tells something about the data. It shows if the hypothesis is true or not true.
How to take random samples?
Random samples: means that every subject in the population has the same chance of being
selected in the sample.
How large should the sample be?
How to check and validate the data? (Typos and other errors when entering data)
Experimental design
