TL:DR: Global water temperature data from <1940 was collected by sailors collecting buckets of water from the ocean and recording the temperature of their bucket water. But some recorded data was rounded (thanks, Air Force!). Then, researchers had to transform their data.
Here is the original research, published in Nature. NPR covered the research article. Reporter Rebecca Hersher didn't discuss the entire research paper. Instead, she told the story of how the researchers discovered and corrected for their flawed ocean water temperature data.
This story might be a little beyond Intro Stats, but it tells the story of messy, real archival data used to inform global climate change and b) introduces the idea of data transformations. Below, I will highlight some of the teaching items.
Systematic bias: The data were all flawed in the same way as they were transcribed without any data to the right of the decimal point. Which is not ideal, but you can correct it.
Archival data: Sometimes, the data you need already exists somewhere. We have only been tracking weather for 100 years. But now we have this opportunity for older data, right, so we can better understand the larger cycle of global water temperatures.
I also like how they could tell that something was happening in their data but didn't know WHY something was happening in their data.
Well-annotated code books are good. That Air Force PDF uncovered by the researchers' friends is also a good example of why you must maintain a codebook and extensive notes on how you treat your data.
Research is so much more than numbers. This is the story of graduate student Duo Chan and his efforts to make his archival data as accurate as possible. It sounds like it was a pain in the ass. Such is science.
Measurement techniques. We can accurately measure water temperature now. I assume that it involves sharks with lasers on their heads. I don't know, but I can assume that we've moved on from The Bucket Method, which was their best at the time.
Data transformations can be tricky to explain to the novice, like transformations to make data less skewed. This could be a simple way to introduce the topic. Yes, I know that actual transformation is much more involved than this, but it is a simple way to introduce the topic.
^Go to the 3 minute mark to see the bucket-boat-water-temperature technique in action
Here is the original research, published in Nature. NPR covered the research article. Reporter Rebecca Hersher didn't discuss the entire research paper. Instead, she told the story of how the researchers discovered and corrected for their flawed ocean water temperature data.
This story might be a little beyond Intro Stats, but it tells the story of messy, real archival data used to inform global climate change and b) introduces the idea of data transformations. Below, I will highlight some of the teaching items.
Systematic bias: The data were all flawed in the same way as they were transcribed without any data to the right of the decimal point. Which is not ideal, but you can correct it.
Archival data: Sometimes, the data you need already exists somewhere. We have only been tracking weather for 100 years. But now we have this opportunity for older data, right, so we can better understand the larger cycle of global water temperatures.
I also like how they could tell that something was happening in their data but didn't know WHY something was happening in their data.
Well-annotated code books are good. That Air Force PDF uncovered by the researchers' friends is also a good example of why you must maintain a codebook and extensive notes on how you treat your data.
Research is so much more than numbers. This is the story of graduate student Duo Chan and his efforts to make his archival data as accurate as possible. It sounds like it was a pain in the ass. Such is science.
Measurement techniques. We can accurately measure water temperature now. I assume that it involves sharks with lasers on their heads. I don't know, but I can assume that we've moved on from The Bucket Method, which was their best at the time.
Data transformations can be tricky to explain to the novice, like transformations to make data less skewed. This could be a simple way to introduce the topic. Yes, I know that actual transformation is much more involved than this, but it is a simple way to introduce the topic.