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Collect fly eggs every week and split eggs into two vials:
Group A: standard food
Group B: diluted food
Rear them this week and measure 30 wings total (say 15 per group).
Week-specific parameters
\[ \mu(P_{week},standard) \quad \mu(P_{week},diluted) \]
Differences in means (parameter)
\[ \Delta_{week} = \mu(P_{week},diluted) - \mu(P_{week},standard) \]
Differences in means (sample estimates)
\[ \hat{\Delta}_{week} = \bar{x}(P_{week},diluted) - \bar{x}(P_{week},standard) \]
Protocol-level parameters
\[ \mu(P_{protocol},standard) \quad \mu(P_{protocol},diluted) \]
Differences in means (parameter)
\[ \Delta_{protocol} = \mu(P_{protocol},diluted) - \mu(P_{protocol},standard) \]
Differences in means (sample estimates)
\[ \hat{\Delta}_{week} = \bar{x}(P_{week},diluted) - \bar{x}(P_{week},standard) \]
same computed difference, but totally different inferential meaning
Population = the set you want to talk about
Parameter = the true value in that population
Statistic (sample estimate) = what you calculated from your sample
Inference = the bridge from sample statistic to population parameter
| Scenario | Response variable | Explanatory variable | Error term |
|---|---|---|---|
| Does temperature affect enzyme activity? | Enzyme activity (e.g. reaction rate) | Temperature | Biological variation, measurement error |
| Does fertiliser concentration affect plant height? | Plant height | Fertiliser concentration | Genetic variation, microenvironment, measurement error |
| Does food type affect wing length in flies? | Wing length | Food type (standard vs diluted) | Developmental noise, genetic variation, measurement error |
| term | est_m1 | p_m1 | est_m2 | p_m2 |
|---|---|---|---|---|
| (Intercept) | 3.810 | 0.711 | 294.456 | 0 |
| bodyweight | 3.562 | 0.000 | -1.096 | 0 |
| altitude | NA | NA | 0.146 | 0 |