Difference between revisions of "Experiment VS Observational study"
(Created page with "Experimenting is to formally state spell out and control for as many variables as you can. Science. Because user makes sure to change nothing else during the experiment many...") |
m |
||
| Line 2: | Line 2: | ||
Second type is observational study. Just monitoring all the variables without a specific structure or interventions. This is what most health trackers do and is useful for many reasons. Also may remove placebo and stress or nervousness effect from a more formal experiment. Unfortunately this is where all those issues I mentioned in [[Finding relations between variables in time series]] and many more in the links all are relevant. | Second type is observational study. Just monitoring all the variables without a specific structure or interventions. This is what most health trackers do and is useful for many reasons. Also may remove placebo and stress or nervousness effect from a more formal experiment. Unfortunately this is where all those issues I mentioned in [[Finding relations between variables in time series]] and many more in the links all are relevant. | ||
| + | |||
| + | When you are done, do not forget to visualize. [[Visualizing your Data]] | ||
Revision as of 19:22, 25 March 2022
Experimenting is to formally state spell out and control for as many variables as you can. Science. Because user makes sure to change nothing else during the experiment many issues with analyzing time based relations do no happen. Consequently linear regression, ttest, causal analysis and breakpoint detection work. Maybe some complications in establishing baseline. See T-test.
Second type is observational study. Just monitoring all the variables without a specific structure or interventions. This is what most health trackers do and is useful for many reasons. Also may remove placebo and stress or nervousness effect from a more formal experiment. Unfortunately this is where all those issues I mentioned in Finding relations between variables in time series and many more in the links all are relevant.
When you are done, do not forget to visualize. Visualizing your Data