> For the complete documentation index, see [llms.txt](https://help.sbtinstruments.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.sbtinstruments.com/mpd/workflows/best-medium/inspect-data-critically.md).

# Inspect data critically

We use a simple mean-based approach to identify the best growth medium. In principle this treats the three time points as pseudo-replicates. It is a nice and simple approach, but there are caveats. Below we explain which pitfalls you should be especially aware of.

{% stepper %}
{% step %}

#### Inspect for increasing trends in the data

If there is an increasing trend in concentration from the first to the subsequent sampling points, this indicates that some of the measurements were not within stationary stage.
{% endstep %}

{% step %}

#### Inspect the coefficient of variation (CV)

If the [coefficient of variation](https://en.wikipedia.org/wiki/Coefficient_of_variation) (CV) is larger than 20% this indicates that some of the measurements were not in stationary growth phase or that the sample workup is associated with high variability.
{% endstep %}

{% step %}

#### Inspect the standard deviation

If the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) error bars overlap this indicates that the growth media reach similar carrying capacity.
{% endstep %}
{% endstepper %}

## Summary

Hopefully, one of your growth media still performs markedly better than the others and is free from issues with increasing trends, high CV, or overlapping error bars.

On the other hand - if the test results are inconclusive - we recommend repeating the experiment in triplicate for each time point, or ideally, conducting biological triplicates for each growth medium.

In the last [Summary](/mpd/workflows/best-medium/summary.md) section we list the the key assumptions and give recommendations for other workflows to further characterize the best growth medium.


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