Earlier in the course, we covered the whole data analysis workflow
and all its components in a big-picture manner, trying to look ahead.
Back then, the topics were abstract. At this stage, we have covered each
topic and worked on specific examples. Now is a good time to re-read the earlier unit.
Hopefully, upon re-reading, some of the topics that were theoretical
and abstract back when you first read it are now clearer and more
Here, I want to briefly repeat and re-iterate the main components.
I’ll keep things short and focus on the most important parts. Now that
you have some experience with all the components yourself, this might
make more sense and you can better appreciate what it means in practical
The Data Analysis workflow
While I hope you appreciate by now that every data analysis is
different, and you need to make lots of decisions along the way. For
each, there is often more than one possible reasonable choice, but there
are some components that I think are needed for any high quality
project. Those can be seen as the basic ingredients of a good data
analysis, with lots of freedom within each category.
I mentioned this point previously, probably more than once 😄: This
need to make choices and decisions on how exactly to perform various
steps of an analysis is one reason why p-values are generally
meaningless, or at least do not mean what people think/claim they mean.
For some good and easy introductions to that topic, see the “Pitfalls”
section on the General Resources
Having a good question (hypothesis) that is interesting,
important, new, and can be answered with the resources you have (data,
skills, computing power) is the most crucial part of any
project. You can do an analysis that is technically perfect,
but if you don’t answer an interesting and relevant question, nobody
will care. While I think one should use state-of-the art analysis
approaches as much as possible, it is in my opinion more important to
answer a good question. I believe that an important question analyzed
with a simple model is almost always better than using a complicated
model to answer a question that nobody cares about. Of course, the
simple model still needs to be reasonable. If one uses a completely
wrong model or performs a faulty analysis, the whole project/paper might
also be meaningless.
You should do your whole analysis as automated, reproducible, and as
well-structured and well-documented as possible. Your colleagues, your
readers and your future self will thank you for it. We have used tools
in this course (R/R Markdown/GitHub) which help in performing an
analysis in such a way. Many other tools are available. While some tools
are worse than others (e.g., Excel), in the end it doesn’t matter too
much what tools you use, as long as you can do things automated,
reproducible, and well-documented.
As you will likely appreciate by now, getting the data into a shape
that can be analyzed is – for almost any dataset – time consuming and
also incredibly important. Lots of mistakes happen at that stage. For a
recent prominent example where things went wrong, see e.g. this
JAMA article, where a mis-coding of a factor variable led to one
conclusion, and upon fixing it, the conclusion changed in the other
direction, leading to retraction of the original study (and
republication of the corrected study). This is an example where an error
was found and fixed. Unfortunately, there are probably a lot of studies
in the literature where mistakes during the wrangling process were made,
wrong results published, and nobody noticed. It is impossible to fully
prevent making mistakes. But there are ways to try and minimize those
problems. To do so, follow these rules:
- Document everything very well. Every step in the wrangling/cleaning
process should be explained and justified (e.g. if you drop observations
with NA, what does it mean and why do you think it’s ok to do).
- Automate things as much as possible. Manual tasks often introduces
- Make everything reproducible. That helps you and others spot
- Critically evaluate every step you take. If something is happening
that doesn’t look quite right, or you get warning messages in your code,
stop and figure out what is going on. Only proceed once you know exactly
what is happening and are ok with it.
- Try different alternatives. For instance if you are unclear if you
should remove missing observations, or remove a variable that has a lot
of missing, or use imputation, why not try it all 3 ways? It usually
doesn’t take much extra work to do a few alternatives. If each version
of doing things gives you more or less the same results, it helps
convince yourself and the readers that your finding might be robust to
the details of the analysis. If different reasonable ways of doing the
analysis lead to different results, you have learned something too, and
it might be worth digging deeper to understand why results differ. You
might find some new, unexpected and interesting bit of science lurking.
It is important to report an any analysis you did, even if just briefly
in the supplement. You are not allowed to run multiple analyses and then
just report the one that gives you the answers you want (likely happens
often, see p-hacking above.)
You have learned that there are a lot of different analysis
approaches out there, and which one to choose depends on many factors,
such as the question (e.g. do you care more about interpretability or
performance), available resources, and many other considerations. All
the rules listed above for wrangling hold for the analysis bit too. Make
it reproducible, well documented, well explained and justified. Make
sure you understand results at each step. If possible, try different
alternative approaches. Some additional, analysis-specific
considerations are the following:
- Think carefully about the performance measure you want to fit to.
While the ‘standard’ ones, like RMSE/SSR for continuous outcomes and
accuracy for categorical outcomes are at times ok, often other measures
might be more meaningful. E.g. for continuous outcomes, you might want
to do RMSE not on the outcome but the log of the outcome. Or you might
want to penalize with least absolute difference to better deal with
outliers. Similarly, for categorical outcomes, especially when there is
imbalance in the data and you have much fewer of one category than of
others, using accuracy might not be best. Some other metric such as F1
score, or a custom performance measure might be better. Spend some time
thinking about the best performance measure before you do all your
- Once you picked your performance measure and are ready to fit/train
your model, make sure to not evaluate performance on the data used for
building the model. More complex models can always give improved
performance on the data used to build the model, thus this metric is not
meaningful! Instead, to evaluate model performance, ideally use some
version of cross-validation, i.e. fitting the model to some of
the data and evaluating model performance on a part of the data that was
not used for fitting. If this is not possible, e.g. because you
don’t have much data or it takes too long to run, us AIC & Co. as a
backup option to determine model quality.
- Compare your model to baseline/null models and simple
single-predictor models to get an idea for the improvement you can get.
Try a complex model to estimate the upper bound of model performance.
Then try a few reasonable models “in between” the null model and the
really complex model, and pick the one that works overall best for your
purpose. That last step is subjective. That is ok, as long as you can
explain and justify why you ended up going with the model you
- Once you have chosen your best model (or even before, for the
purpose of picking your final model), perform model assessment. Look at
uncertainty, investigate residuals, look at variable importance, etc.
Poke your model in as many ways as possible to understand how it works
and what its limitations might be.
- If you have enough data, set some aside at the beginning (test
data), and apply your model to that data at the very end. This gives you
the most honest assessment of your model performance for new/unseen
You almost never do an analysis just for fun and for yourself.
Usually, there are other reasons. For instance in academia/science, we
analyze data to better understand our system, to test hypotheses, to
answer questions. Similarly in industry and other applied settings, we
analyze data to come to actionable conclusions (e.g. we determine which
images show a likely cancer and therefore which patients need further
tests or surgery). In all those situations, we want to communicate our
findings to others. That can be through the peer-reviewed literature, in
a meeting with our colleagues and bosses, as a report for patients, etc.
Being able to report findings from a potentially complicated analysis in
a way that it has an impact, and is appropriate for the right audience,
is not easy. Some of the ideas listed above, as well as others are worth
keeping in mind:
- Present your findings in such a way that people can (but don’t have
to) go deeper easily. Start with a short summary (often called
executive summary in industry and abstract in
academia). This short write-up should summarize your findings
understandably and honestly. Do not spin/hype things that are not
supported by your actual analysis. Also, focus on the main important
finding(s) and their implications. The main deliverable (usually some
form of written report or a paper), should present all the main findings
and steps you took, nicely explained. Then provide additional
information (e.g. supplement, appendices) with more details. Finally,
provide all the raw materials, i.e. data and well-documented code, for
others to look at. By layering content, different audiences can go into
your findings in as much or little detail as they want.
- Explain and justify everything. It’s unlikely that everyone would
have made exactly the same decisions you did during your analysis. But
by explaining your rationale, readers can decide if they find what you
did reasonable, and thus make an informed decision as to how much they
trust your findings.
- Report results from multiple approaches: If you show how certain
decisions during the analysis do or don’t affect the results, it makes
things more transparent and can instill greater confidence in
- Automate things. As much of your final products as possible should
be automated. That means that figures and tables should not be created
by hand. This way, if you want or have to change things upstream
(e.g. you noticed a mistake in some analysis step or reviewers/your boss
request changes), you can update everything as quickly, automated and
seamless as possible.
- Use modern ways to report and disseminate your findings. The
standard academic way is still to write peer-reviewed papers, or in
industry, prepare a report. However, such documents are generally not
too widely read and at times have only limited impact. As appropriate
for your project, consider other sources of dissemination. For instance,
make a website for your analysis. Turn it into a blog post. Tweet about
it. Use interactive tools (e.g. the
R Shiny package) to allow the audience to interact with your
results. Be creative and think about the best ways to reach your