1) The first interesting point I read was in Sean Mussenden’s 10 common mistakes in data journalism. The first common mistake was “don’t overestimate the meaning of your data”. My first question last week on data journalism was if data journalism was going to aid in stories or become the story. I thought that data journalism could be magnificent tool in helping convey points in a story but having it be the entire story would be redundant. Also, data doesn’t always tell the complete truth. Data can be skewed in ways that benefit a person’s point. Like in the case of police brutality. A person who doesn’t believe that blacks are more likely to face police brutality can make a data set that says that more whites are killed by police than blacks per year and it would be the truth. But what they fail to mention is the anount of whites and blacks in America. There are many more whites than blacks in the US so the data would show that whites get killed more but in reality, the ratio would be much higher in the case of black people being killed by police.

2) An important aspect of data journalism is credibility. Knowing where the original data can from, how old it is and the reason why it was collected, among other things, are very important questions to ask when reading and creating data journalism.