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Michael Dryden, veterinary students were given positive roundworm and hookworm samples and asked to perform fecal examinations using one of three techniques: 1 direct smear, 2 simple flotation or 3 centrifugal flotation. In every instance, only centrifugal flotation techniques achieved an acceptable level of accuracy. Fecal sample size and consistency. Size matters, and too small a sample can compromise results.

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While fecal loops or rectal thermometers often are used to obtain samples, the average sample size obtained with these methods is only about one-tenth of a gram. In fact, the ideal sample size for testing is one gram of formed feces a cube measuring approximately one-half inch on a side.

Avoiding the Pitfalls of Familiarity

Examination of feces containing a higher fluid content soft, unformed or diarrheic feces requires a larger sample, since liquid dilutes parasite eggs. In a number of cases, sample size and consistency cannot be controlled. Flotation solutions. The density of the different flotation solutions can affect parasite egg and larvae recovery.

You do not have human resources personnel in each state, and you often are called upon to render advice regarding the legality of various employment decisions. You receive a call from a manager of one of your restaurants located in Washington D. He is in the process of hiring a new waitress and is deciding between two female candidates with no known disabilities who are the same age, race, and national origin. Candidate 1 has several years of experience as a waitress, while Candidate 2 has virtually none.

The restaurant is located close to several Georgetown University fraternities and its customers are predominantly male. The manager feels that Candidate 2 who is more physically attractive than Candidate 1 would draw more male customers into the restaurant. The manager asks you whether it is illegal to make a hiring decision based on the physical attractiveness of a candidate.

Human Rights Act of , D. Your Name required. The world that measured and collected earthquakes in the early 20th century was very different than the one that did so in the last decade. If we separate the line plot by magnitude and add annotations that describe advances in seismology, we see that the rise is only in the smaller group magnitude 6.

When it comes to earthquakes, the gap between data and reality is getting smaller.

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Everyday on my way to work I walk across the Fremont Bridge. Since it sits so close to the water, it opens on average 35 times a day, which supposedly makes it the most opened drawbridge in the United States. The city also provides hourly counts going back to October 2, at data. Downloading this data and visualizing it yields the following timeline:.

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I showed this data at a recent luncheon of the Puget Sound Research Forum , and asked what the attendees thought of these spikes. Notice how each of these ideas is based on the assumption that there actually were more bikes that crossed the bridge on those days.

David Bauer was in the audience and found the answer for us: equipment error. The counters just glitched for a few hours on both days. You can read all the details of these anomalous readings and the correspondence between a local blogger and a city employee at the Seattle Bike Blog. Turns out a low battery was the culprit. This past year, the whole world watched in horror as Ebola ravaged West Africa.

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In any case, the WHO provides data about fatalities in weekly situation reports. I had an interesting discussion on twitter with Alex McDonnell about this data. In it he referred to errors in the WHO reports. You bet. Notice the drops in cumulative death counts — the handful of times when the lines slope down:. Of course it makes perfect sense: the task of diagnosing disease and ascertaining causes of death in some of the more remote locations, where the equipment and staff are often severely limited, must be incredibly difficult.

Here are the criteria:. Far from it.

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This example merely demonstrates that the gap between data and reality can exist even when the stakes are high. Classifying diseases and deaths in chaotic conditions can be tricky business indeed. Notice that in these three examples — 1.

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Visualizing the data can be one of the best ways to find problems with it. Earlier in the game, though, it helps to remind ourselves that every data point that exists was collected, stored, accessed, etc, via imperfect processes. Here are six seven suggestions to help you avoid confusing data with reality:. Ultimately, each data collection activity is unique, and there are too many possible sources of error to list them all. Do we arrogantly or naively see ourselves as experts on a topic as soon as we get our hands on some data, or do we humbly realize that our knowledge is imperfect, and we may not know the full story?

What we can do, though, is seek to identify any gaps that may exist, and take that into account when we use data to form our opinions. Next: Part 2: Fooled by Small Samples. Tags: avoiding data pitfalls.

Avoiding Pitfalls of Familiarity

Great post Ben, and a helpful reminder. Thank you for this post. A great reminder, Ben, of what we all need to be more vigilant about.