I also agree with Kathryn. Being critical is very important when dealing with data. Researchers need to be able to recognize the flaws as used in the article.

Context matters! We’ll talk about that more this semester. And great connection with the inquiry journals!

Love the connection you are making with what we discussed in class!

Del – what a great insight. All of this talk about what can go wrong can be really depressing and make us want to give up. But like we talked about in our workshop, there are troubling things that happen when we don’t use data and “check out.” I hope that we’ll all be inspired this semester to keep engaging in ways that are helpful.

Yes and these paragraph also reminded me of the interesting insights you all had in our workshop this week…when we were talking about how we need the media and headlines and yet the media and headlines also can cause problems.

In the upcoming modules, you’ll learn a little more about the pressure that researchers and publishers of journals often experience – pressure to have big headlines. For those of you who like to think about capitalism and the many critiques of it, you might find it interesting to think about how research, like pretty much everything else, is an industry where some people benefit.

We’ll be talking a lot about credible sources next week. It’s an important step in creating our own research proposal. Hopefully, we will do a better job than the folks in this article did!

Just like in my first comment I expressed how I agree with the author in believing that we must be better when encountering these different statistics. Having a plan and it being in order will help ensure better data for researchers’.

I think the author of this article is right. We should not give up on the numbers but be better. There are things we can do in a specific order in able to have better data and research outcomes.

Being critical is crucial when interpreting data. It is important to have healthy background knowledge on how to differentiate good and bad statistics, as well as knowing what is considered a ‘good’ or ‘bad’ statistic.