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add up next at the end of errorbar1 and errorbar2
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‎docs/activities.html‎

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‎docs/projects/errorbar1.html‎

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<li><a href="#why-this-matters" id="toc-why-this-matters" class="nav-link" data-scroll-target="#why-this-matters">Why this matters</a></li>
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<li><a href="#how-much-disagreement-is-a-lot" id="toc-how-much-disagreement-is-a-lot" class="nav-link" data-scroll-target="#how-much-disagreement-is-a-lot">How much disagreement is a lot?</a></li>
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<li><a href="#up-next-are-my-error-bars-big-or-small" id="toc-up-next-are-my-error-bars-big-or-small" class="nav-link" data-scroll-target="#up-next-are-my-error-bars-big-or-small">Up next: Are my error bars big or small?</a></li>
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<p>An important detail is that the error bar visualizes the uncertainty both up and down. In other words the error bar shows the possibility both that the measured value could have been larger and the uncertainty that the measured value could have been smaller. We only need to divide by the uncertainty so you would divide the difference by <b>half</b> of the vertical height of the error bar.</p>
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<p>To give a concrete example, earlier we discussed that a tire pressure gauge is measuring 38.5 PSI +/- 0.05 PSI. So if we visualized the error bar for that measurement it would be centered at 38.5 PSI and it would go up +0.05 PSI and down -0.05 PSI. The vertical height of the error bar would be 1.0 PSI. If we compared the 38.5 PSI measurement to some kind of model and there was a difference, we would divide that difference by 0.5 PSI (and not 1.0 PSI) to get an idea for how different the measurement is from the model.</p>
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<h2 class="anchored" data-anchor-id="up-next-are-my-error-bars-big-or-small">Up next: <a href="errorbar2.html">Are my error bars big or small?</a></h2>
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‎docs/projects/errorbar2.html‎

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<li><a href="#task-example-data" id="toc-task-example-data" class="nav-link" data-scroll-target="#task-example-data">Task: Example data</a></li>
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<li><a href="#things-to-notice" id="toc-things-to-notice" class="nav-link" data-scroll-target="#things-to-notice">Things to notice</a></li>
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<li><a href="#advanced" id="toc-advanced" class="nav-link" data-scroll-target="#advanced">Advanced</a></li>
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<li><a href="#up-next-error-bars-and-animal-sizes" id="toc-up-next-error-bars-and-animal-sizes" class="nav-link" data-scroll-target="#up-next-error-bars-and-animal-sizes">Up next: Error bars and animal sizes</a></li>
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<h2 class="anchored" data-anchor-id="advanced">Advanced</h2>
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<p>What we have done is obtained the error bar from the measurements rather than obtaining the error bar from our knowledge of the measurement methods we are using. This is not the only way to obtain error bars from the measured data. Another more complicated approach is called “bootstrapping”.</p>
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<p>Another related situation is when you are shopping on a site like Amazon and you see a seller that has 5/5 stars but only 10 reviews, and another seller that is 4.8/5 stars with 50 reviews and another with 4.65/5 stars with 200 reviews. This is addressed in an <a href="https://www.youtube.com/watch?v=8idr1WZ1A7Q">excellent video</a> by 3Blue1Brown. In that video he provides some math to back up what most of us already know which is that a 100% rating with only 10 reviews is not very impressive (because with so few reviews the error on the mean is very large).</p>
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<section id="up-next-error-bars-and-animal-sizes" class="level2">
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<h2 class="anchored" data-anchor-id="up-next-error-bars-and-animal-sizes">Up next: <a href="errorbar3.html">Error bars and animal sizes</a></h2>
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‎docs/search.json‎

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"title": "Error bars and animal sizes",
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"section": "Other resources",
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"text": "Other resources\nGo to dataclassroom.com and search for Cicada data"
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},
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{
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"objectID": "projects/errorbar2.html#up-next-error-bars-and-animal-sizes",
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"href": "projects/errorbar2.html#up-next-error-bars-and-animal-sizes",
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"title": "Are my error bars big or small?",
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"section": "Up next: Error bars and animal sizes",
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"text": "Up next: Error bars and animal sizes"
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{
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"objectID": "projects/errorbar1.html#up-next-are-my-error-bars-big-or-small",
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"href": "projects/errorbar1.html#up-next-are-my-error-bars-big-or-small",
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"title": "Why we love error bars!",
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"section": "Up next: Are my error bars big or small?",
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"text": "Up next: Are my error bars big or small?"
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}
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‎projects/errorbar1.qmd‎

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To give a concrete example, earlier we discussed that a tire pressure gauge is measuring 38.5 PSI +/- 0.05 PSI. So if we visualized the error bar for that measurement it would be centered at 38.5 PSI and it would go up +0.05 PSI and down -0.05 PSI. The vertical height of the error bar would be 1.0 PSI. If we compared the 38.5 PSI measurement to some kind of model and there was a difference, we would divide that difference by 0.5 PSI (and not 1.0 PSI) to get an idea for how different the measurement is from the model.
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## Up next: [Are my error bars big or small?](errorbar2.html)
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‎projects/errorbar2.qmd‎

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What we have done is obtained the error bar from the measurements rather than obtaining the error bar from our knowledge of the measurement methods we are using. This is not the only way to obtain error bars from the measured data. Another more complicated approach is called “bootstrapping”.
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Another related situation is when you are shopping on a site like Amazon and you see a seller that has 5/5 stars but only 10 reviews, and another seller that is 4.8/5 stars with 50 reviews and another with 4.65/5 stars with 200 reviews. This is addressed in an [excellent video](https://www.youtube.com/watch?v=8idr1WZ1A7Q) by 3Blue1Brown. In that video he provides some math to back up what most of us already know which is that a 100% rating with only 10 reviews is not very impressive (because with so few reviews the error on the mean is very large).
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## Up next: [Error bars and animal sizes](errorbar3.html)

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