Misconceptions in Data Analysis: Study Hall Data Literacy #2: Crash Course + Study Hall

[@BestICan]

Kudos to Jessica, Kristy, and the team who created this course. At a time of critical need, you folks produced high quality and engaging content. Thank you!

[@janeferry286]

Thank you for expressing my thoughts.😊

[@Kimi252]

Dude just summarised 3 weeks worth of content from the stats course im taking. And explained confounding so well too!!

[@tapdance6767]

Just found you guys and I’m impressed. Wow. Thanks for the incredible content.

[@newbestofthis4422]

I love it. Those who like this one you will enjoy crash course statistics

[@douglasodipo6557]

This content is gold. Please keep at it.

[@amit_yadav_]

Informative, thank you. How do we find if independent agencies are independently sharing output.

[@fernandocortes1187]

1:45. correlation and causation

[@conspirators1]

Gosh, I needed this so badly for my statistical analysis in my thesis.

[@potternutmania]

How do we decide small vs large amounts of data, is there a clear line or do we just rely on intuition

[@user-te7zz8mv3x]

Thanks for making the concepts so much clearer!

[@feniciadacosta4450]

Hi this video interested, could you please explain how to Ideing a feasible resntifyearch question suitable for a literature review?

[@nonamesl3f7duuude]

Very well done, super interested in sharing more from this series with our kiddos

[@jotagalvan]

Im just excited, 3th ep and still rocking

[@etharyasser]

Now every time i’m presented by a data finding i’ll be the most amazing detective/ genius

[@arizonastateuniversity]

Awesome. New videos on Data Literacy are added to our channel every week! In case you wanted to learn more on this topic, check out the full playlist here: kzbin.info/aero/PLNrrxHpJhC8m_ifiOWl1hquDmdgvcviOt Tap or click "Save" to add it to your KZbin library. :)

[@artjohnson01]

I enjoyed this. It was a nice refresher. Play at 90% speed for best comprehension (she talks pretty fast).

[@yuta2349]

This series is so damn helpful !

[@cotedubois]

This stuff is darn good!

[@arizonastateuniversity]

Thanks for the feedback! You can check out the latest episode for Study Hall: Data Literacy here: kzbin.info/www/bejne/nZLMhXWHftWjjs0 or subscribe to our channel to find a range of new Study Hall content on a regular basis: kzbin.info

[@bifurioussiren]

thank you! such great content

[@via1408]

I hope this video gets on the tending page

[@abdirahmanmohameddahie3008]

More helpful thank you so much.

[@arizonastateuniversity]

Glad you're finding the content valuable! If you haven't already, feel free to subscribe to our channel to find a range of new Study Hall content on a regular basis: kzbin.info

[@denissediaz3640]

Good god this topic is so incredibly boring but so incredibly important to learn. I’m thankful for these videos, the world would be a better place if people were even a little bit more literate

[@StarlitFran]

Correlation is not causation!!! I need a tshirt now!!

[@amanshukla8758]

Damn this is great !

[@shahriariftekhar2558]

where is subtitle

[@Proctole]

As a non-American, can someone help me understand how race can be a confounding variable?

[@sushilverma2634]

plz explain mean mode and median as well

[@thekingwhostitches]

Why does she alternate between saying “day-ta” and “data”?

[@k.alipardhan6957]

we have to ignore the law of small numbers in the real world, often more data is expensive/problematic

[@resipsaloquitur13]

This should be a topic in HS. Or maybe it is...I’m old.

[@ilovemusic3584]

2nd

[@serhiyint]

2.5/5 stars. Teaching data literacy is a great pursuit but unfortunately there are some imperfections here IM_H_O. Too many unnecessary animations and sound effects that distract while not having an obvious function, using tiredness and untidiness to visually portray hoaxer and forming harmful stereotypes this way, using income to indirectly compare people. And yes, this comment may not be ideal either, I'm not a native speaker, comment with corrections please.

[@alessandromorelli5866]

what is so wrong about comparing people based on income? it's data analysis, if a correlation really does exist then it can be found. stereotypes are bad as they are oversimplifications of real data that might even be wrong, but if a correlation or causation really does exist between two sets of data then it's not a stereotype anymore