<p>Contents</p>
<p><strong>I
The crisis of confidence in social and life sciences: State of affairs (4 September 2019)</strong></p>
<p>Learning objectives: Become acquainted with the recent developments regarding the so-called “replication crisis”.</p>
<ul>
<li>Replication crisis: how it all started (this time around).</li>
<li>Medicine, you were supposed to be the best of us!</li>
<li>Consequences of problematic practices.</li>
<li>You’re not alone in misinterpreting p-values.</li>
</ul>
<p><strong>II
From questionable research practices and biased stories, to better evidence and/or decisions (11 September 2019)</strong></p>
<p>Learning objectives: Understand what the research community is doing to improve the quality of published research. Extrapolate to non-academic settings.</p>
<ul>
<li>Transparency and Openness Promotion (TOP) guidelines to fight bad science.</li>
<li>Transforming publication practices with pre-prints</li>
<li>Disentangling confirmatory and exploratory research.</li>
<li>Tricky rule-of-thumb questions to ask when being presented research (1/2: “null findings”).</li>
</ul>
<p>III
<strong>Magnificient mistakes and where to find them (18 September 2019)</strong></p>
<p>Learning objectives: Recognise some particular pitfalls in evidential statements. Understand that decisions in the field do not need to rely on correct predictive statements, let alone scientific evidence.</p>
<ul>
<li>Tricky rule-of-thumb questions to ask when being presented research (2/2: “statistically significant” findings).</li>
<li>Ways tests can fail: Type I/II mistakes. Type M and Type S mistakes.</li>
<li>The difference between evidence of absence and absence of evidence: Black Swans and the Turkey Problem.</li>
<li>When you don’t need to be right: green lumber, and a first taste of convexity.</li>
<li>Heuristics: Simple rules that make us smart.</li>
</ul>
<p><strong>IV
On interpreting data nudes instead of summary tables (25 September 2019)</strong></p>
<p>Learning objectives: Understand the rationale for visualising data, and what can be hidden when reporting summary statistics only. Learn to spot some common tricks used to visualise data in a favourable way to the presenter.</p>
<ul>
<li>A crude redux to evidence of absence. </li>
<li>Data Nudes vs. Shitty Tables.</li>
<li>The End of Average. </li>
<li>What gets lost in looking at numbers alone: Uncertainty hidden in the absence of distributions. </li>
<li>Demons with(in) axes: Slaying or summoning effects with presentation tricks.</li>
<li>Dose-response effects masked by averages.</li>
</ul>
<p><strong>V
Complex systems and why they ruin everything straightforward (2 October 2019)</strong></p>
<p>Learning objectives: Become familiar with general features of so-called complex systems. Understand how they can be thought of in the context of practical interventions.</p>
<ul>
<li>Intro to complexity, and general features of complex systems. </li>
<li>Interaction vs. component dominant systems. </li>
<li>Don’t camp at 1st order
effects in dragon season. </li>
<li>Navigating the Four Quadrants</li>
</ul>
<p><strong>VI
Never cross Heraclitus’ river, if it’s on average 1 meter deep: Interventions and their offspring (9 October 2019)</strong></p>
<p>Learning objectives: Understand the rationale behind interventions and experimenting/intervening in complex systems, as well as some limitations of big data.</p>
<ul>
<li>Change comes in a triad. Sales tricks to counter, use and abuse.</li>
<li>Pathway thinking & complexity thinking in behaviour change science.</li>
<li>Failures and unexpected effects of social interventions. </li>
<li>When is it
safe(r) to intervene?</li>
</ul>
<p><strong>VII
Dynamic/idiographic phenomena, and hidden assumptions (16 October 2019)</strong></p>
<p>Learning objectives: Describe the concepts of ergodicity and stationarity. Understand how they can mislead when not taken into account when e.g. assessing risks.</p>
<ul>
<li>Assumptions, schmassumptions; mind your foundations! </li>
<li>Damned world not
sitting still: Ergodicity & stationarity </li>
<li>The idiographic approach to
science </li>
<li>The best map fallacy </li>
<li>The precautionary principle for policy
and interventions </li>
<li>Frequency vs. consequences of being wrong: What
matters more? </li>
<li>Recap on the course: The Fourth Quadrant will find you,
so better put your house in order</li>
</ul>
