sunday: measurement bias

Signed-off-by: Shreeyash Pandey <[email protected]>

Author: bojle

build/doctrees/environment.pickle

@@ -67,3 +67,21 @@ This week: `How to be an effective researcher - Michael Nielsen <https://michael
 * Dont chase public appreciation with your research but at the same time, keep
   in mind what the research landscape as a whole requires. These constitute
   important problems.
+
+July 30, 2023
+-------------
+
+This week: `Producing wrong data without doing anything obviously wrong! - Mytkowicz et. al. <https://dl.acm.org/doi/10.1145/1508284.1508275>`__
+
+* *Measurement bias* is the phenomenon where faulty collection or data or
+  invalid reasoning towards a conclusion as a result of faulty data causes
+  incorrect understanding of a process being investigated.
+* Measurement bias is ubiquitous and more often than not unpredictable.
+* Detection and avoidance tends to be the best strategy to deal with it.
+* Causal analysis is a general technique for determining if we have reached an
+  incorrect conclusion from our data. Three step procedure: Suppose X causes Y
+  is a conclusion. But there is a chance Z could be causing Y. First, create an
+  **Intervention** to X. Change the system and **Measure** X. Now, **Confirm**
+  if Y changes when X changed, if it did, our conclusion is correct.
+* Causal analysis is not a way to acquire flawless data, rather it is to assure
+  ourselves that the conclusions we reach after are valid.

build/doctrees/sunday.doctree

@@ -87,6 +87,24 @@ <h2>July 16, 2023<a class="headerlink" href="#july-16-2023" title="Permalink to
 important problems.</p></li>
 </ul>
 </section>
+<section id="july-30-2023">
+<h2>July 30, 2023<a class="headerlink" href="#july-30-2023" title="Permalink to this heading">¶</a></h2>
+<p>This week: <a class="reference external" href="https://dl.acm.org/doi/10.1145/1508284.1508275">Producing wrong data without doing anything obviously wrong! - Mytkowicz et. al.</a></p>
+<ul class="simple">
+<li><p><em>Measurement bias</em> is the phenomenon where faulty collection or data or
+invalid reasoning towards a conclusion as a result of faulty data causes
+incorrect understanding of a process being investigated.</p></li>
+<li><p>Measurement bias is ubiquitous and more often than not unpredictable.</p></li>
+<li><p>Detection and avoidance tends to be the best strategy to deal with it.</p></li>
+<li><p>Causal analysis is a general technique for determining if we have reached an
+incorrect conclusion from our data. Three step procedure: Suppose X causes Y
+is a conclusion. But there is a chance Z could be causing Y. First, create an
+<strong>Intervention</strong> to X. Change the system and <strong>Measure</strong> X. Now, <strong>Confirm</strong>
+if Y changes when X changed, if it did, our conclusion is correct.</p></li>
+<li><p>Causal analysis is not a way to acquire flawless data, rather it is to assure
+ourselves that the conclusions we reach after are valid.</p></li>
+</ul>
+</section>
 </section>
 
 

build/html/_sources/sunday.rst.txt

@@ -67,3 +67,21 @@ This week: `How to be an effective researcher - Michael Nielsen <https://michael
 * Dont chase public appreciation with your research but at the same time, keep
   in mind what the research landscape as a whole requires. These constitute
   important problems.
+
+July 30, 2023
+-------------
+
+This week: `Producing wrong data without doing anything obviously wrong! - Mytkowicz et. al. <https://dl.acm.org/doi/10.1145/1508284.1508275>`__
+
+* *Measurement bias* is the phenomenon where faulty collection or data or
+  invalid reasoning towards a conclusion as a result of faulty data causes
+  incorrect understanding of a process being investigated.
+* Measurement bias is ubiquitous and more often than not unpredictable.
+* Detection and avoidance tends to be the best strategy to deal with it.
+* Causal analysis is a general technique for determining if we have reached an
+  incorrect conclusion from our data. Three step procedure: Suppose X causes Y
+  is a conclusion. But there is a chance Z could be causing Y. First, create an
+  **Intervention** to X. Change the system and **Measure** X. Now, **Confirm**
+  if Y changes when X changed, if it did, our conclusion is correct.
+* Causal analysis is not a way to acquire flawless data, rather it is to assure
+  ourselves that the conclusions we reach after are valid.

build/html/searchindex.js

@@ -87,6 +87,24 @@ <h2>July 16, 2023<a class="headerlink" href="#july-16-2023" title="Permalink to
 important problems.</p></li>
 </ul>
 </section>
+<section id="july-30-2023">
+<h2>July 30, 2023<a class="headerlink" href="#july-30-2023" title="Permalink to this heading">¶</a></h2>
+<p>This week: <a class="reference external" href="https://dl.acm.org/doi/10.1145/1508284.1508275">Producing wrong data without doing anything obviously wrong! - Mytkowicz et. al.</a></p>
+<ul class="simple">
+<li><p><em>Measurement bias</em> is the phenomenon where faulty collection or data or
+invalid reasoning towards a conclusion as a result of faulty data causes
+incorrect understanding of a process being investigated.</p></li>
+<li><p>Measurement bias is ubiquitous and more often than not unpredictable.</p></li>
+<li><p>Detection and avoidance tends to be the best strategy to deal with it.</p></li>
+<li><p>Causal analysis is a general technique for determining if we have reached an
+incorrect conclusion from our data. Three step procedure: Suppose X causes Y
+is a conclusion. But there is a chance Z could be causing Y. First, create an
+<strong>Intervention</strong> to X. Change the system and <strong>Measure</strong> X. Now, <strong>Confirm</strong>
+if Y changes when X changed, if it did, our conclusion is correct.</p></li>
+<li><p>Causal analysis is not a way to acquire flawless data, rather it is to assure
+ourselves that the conclusions we reach after are valid.</p></li>
+</ul>
+</section>
 </section>
 
 

build/html/sunday.html

@@ -67,3 +67,21 @@ This week: `How to be an effective researcher - Michael Nielsen <https://michael
 * Dont chase public appreciation with your research but at the same time, keep
   in mind what the research landscape as a whole requires. These constitute
   important problems.
+
+July 30, 2023
+-------------
+
+This week: `Producing wrong data without doing anything obviously wrong! - Mytkowicz et. al. <https://dl.acm.org/doi/10.1145/1508284.1508275>`__
+
+* *Measurement bias* is the phenomenon where faulty collection or data or
+  invalid reasoning towards a conclusion as a result of faulty data causes
+  incorrect understanding of a process being investigated.
+* Measurement bias is ubiquitous and more often than not unpredictable.
+* Detection and avoidance tends to be the best strategy to deal with it.
+* Causal analysis is a general technique for determining if we have reached an
+  incorrect conclusion from our data. Three step procedure: Suppose X causes Y
+  is a conclusion. But there is a chance Z could be causing Y. First, create an
+  **Intervention** to X. Change the system and **Measure** X. Now, **Confirm**
+  if Y changes when X changed, if it did, our conclusion is correct.
+* Causal analysis is not a way to acquire flawless data, rather it is to assure
+  ourselves that the conclusions we reach after are valid.