Angie B

MODEL (in science): a set of equations or algorithms that takes the same inputs used in an experiment, and outputs numbers that are similar/compatible with the experimental outcomes, or predict the outcomes of future experiments.
- Descriptive model: addressing the ‘what’ question “what are the key variables that determine the observed phenomenon?”
- Mechanistic model: addressing the ‘how’ question “what are the physical mechanisms that produce the observed phenomenon?”
- Normative model: addressing the ‘why’ question “what are the principles from which the observed phenomenon can be derived?  or, why does nature behave the way it does?”
EXPERIMENT (in science): ideally, a set up in which, all but one variables of the system of interest are fixed; the remaining variable is varied under the experimenters control, and changes in the behavior of the system are recorded as a function of the controlled variable. In practice, typically many variables are not under the control of the experimenter, and repeating the same experiment several times will give different outcomes -> statistical analysis of uncertainty, experimental errors/noise, significance
- Exploratory experiment: “let’s try to change this variable and see what happens”
- Hypothesis-based: “I think if I change this variable in this direction, the system will behave in this way; let’s check if that’s true”
- Model-based: “My model predicts that this and that will happen if I run this and that experiment: let’s check if that’s true, and if not I’ll change the model”