+<div id="empirical-questions" class="section level2">
+<h2>Empirical Questions</h2>
+<p>EQ0.</p>
+<ol style="list-style-type: lower-alpha">
+<li><p>The unit of analysis is the customer. The dependent variable is the type of board purchased and the independent variable is gender. Males, females, and unkown gender customers are being compared. This is a two-way test.</p></li>
+<li><p>The null hypothesis is that the board purchased is independent of the gender of the customer. The alternative hypothesis is that if we know the gender of the customer that will tell us something about the type of board they purchased.</p></li>
+<li><p>A <span class="math inline">\(\chi^2\)</span> test found statistically significant evidence that board purchase behavior differs by gender. This difference is convincing, but it does directly not tell us what we really want to know, which is the difference between men and women. It could be possible that it is simply identifying a significant difference in the number of unknown gender customers across board types. Many of these concerns are addressed in the text and with additional tests, giving increased confidence in the reality of this difference.</p></li>
+<li><p>Statistical tests help to give (or take away) confidence in a conclusion. People are not natively good at estimating how likely something is due to chance and tests help us to make these judgments. Choosing a statistical test is based on the question that you want to answer and the type of data that you have available to answer it. For example, if this were numeric data (e.g., the amount of money spent on electronics for men and women) then we could choose a t-test to compare those distributions.</p></li>
+</ol>
+<p>EQ1</p>
+<ol style="list-style-type: lower-alpha">
+<li><p>These are counts for two categorical variables, so the procedure used was a <span class="math inline">\(\chi^2\)</span> test. The null hypothesis is that whether or not a blog is governed by one person is independent of whether it is on the left or the right.</p></li>
+<li><p>It would be surprising to see these results by chance and it make sense to believe that this difference is real. However, the main reason to be skeptical is the way that the data are grouped. The authors could have grouped them differently (e.g., 1-2 people, 3-4 people, and 5+ people); if the decision on how to group was made after seeing the data then we have good reason to be skeptical.</p></li>
+<li></li>
+</ol>
+<pre class="r"><code># First we create the dataframe
+df = data.frame(Governance=c('Individual','Multiple', 'Individual','Multiple'),
+ Ideology = c('Left','Left','Right','Right'),
+ Count = c(13,51,27,38))
+
+# We can make sure it's the same by testing the Chi-squared
+chisq.test(matrix(df$Count, nrow=2))</code></pre>
+<pre><code>##
+## Pearson's Chi-squared test with Yates' continuity correction
+##
+## data: matrix(df$Count, nrow = 2)
+## X-squared = 5.8356, df = 1, p-value = 0.01571</code></pre>
+<pre class="r"><code>percentage_data = df %>%
+ group_by(Ideology) %>%
+ summarize(individual_ratio = sum(Count[Governance=='Individual']) / sum(Count),
+ group_count = sum(Count))
+
+shaw_benkler_plot = percentage_data %>%
+ ggplot(aes(x=Ideology, y=individual_ratio * 100)) +
+ geom_bar(stat='identity', aes(fill=c('red','blue')), show.legend=F) +
+ ylab('Percentage of Blogs') + theme_minimal()
+
+shaw_benkler_plot</code></pre>
+<p><img 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" width="672" /></p>
+<pre class="r"><code># If we want to add error bars, we need to calculate them (Note that ggplot could do this for us if we had raw data - always share your data!)
+
+# I decided to use confidence intervals. The standard error is another reasonable choice
+
+# Remember that for a binomial distribution (we can consider individual/non-individual as binomial), confidence intervals are mu +- x * sqrt((p*(1-p))/n)
+
+ci_95 = 1.96 * sqrt(percentage_data$individual_ratio * (1 - percentage_data$individual_ratio)/percentage_data$group_count)
+
+shaw_benkler_plot + geom_errorbar(aes(ymin=(individual_ratio-ci_95)*100, ymax=(individual_ratio + ci_95)*100),
+ alpha = .3,
+ size=1.1,
+ width=.4)</code></pre>
+<p><img 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" width="672" /></p>
+<p>The error bars do overlap in this case, but that actually doesn’t tell us whether they are significantly different.</p>
+<ol start="4" style="list-style-type: lower-alpha">
+<li>We don’t need to be very worried about the base rate fallacy because the sizes of both groups are about the same.</li>
+</ol>
+</div>
projects / stats_class_2019.git / blobdiff