- `yeast_av_data`: a data frame containing the average (over all cells) of the morphological measures for each strain and each cell cycle phase with the corresponding genotype (same columns as `yeast_data`)
- `yeast_av_data`: a data frame containing the average (over all cells) of the morphological measures for each strain and each cell cycle phase with the corresponding genotype (same columns as `yeast_data`). More details with:
```{r, eval=F}
str(yeast_av_data)
```
- `strain_id`: a data frame containing the identification of the different strains (including other not present in `morpho_data` and `gt_data`)
- `strain_id`: a data frame containing the identification of the different strains (including other not present in `morpho_data` and `gt_data`)
...
@@ -649,7 +653,14 @@ What can you say about this figure? What could be the problem? especially regard
...
@@ -649,7 +653,14 @@ What can you say about this figure? What could be the problem? especially regard
<details><summary>Solution</summary>
<details><summary>Solution</summary>
<p>
<p>
In a non negligible number of samples, the $H_0$ hypothesis was rejected (p-value $\leq\alpha$) whereas it is true. In this case, we find a significant result $\mu\ne 0$ despite being wrong.
In a non negligible number of samples, the null hypothesis was rejected (p-value $\leq\alpha$) whereas it is true. In this case, we find a significant result $\mu\ne 0$ despite being wrong.
However, in the majority of the studies, the null hypothesis is correctly not rejected.
**Important:**
- confirm a detected effect with additional experiments/studies
- the more (independent) studies, the lower risk of incorrect conclusion