Math Extra Credit Essay
Alex Bolstrum
11/5/14
1st
Math Extra Credit Essay In the graphs provided it is very obvious that the author in the second graph has lied. The graphs were exactly the same besides the fact that in the second graph the author added a break in the graph which completely skews the data. Instead of looking like a modest profit growth like the first graph it looks like a huge increase in profit because the whole graph is not shown. The scale on the first graph is bigger so it doesn't look like as big as a jump compared to the second graph. Statistics can be used in many different ways to deliberately mislead the audience. One major way is by adding a break in a graph as seen above. Another way to deliberately mislead the audience is to change the scale of the graph which makes it looks like it went up or down more or less than it really did. Also, another way the audience can be deliberately fooled is by using a bad sample of data. If you ask people questions and you know they will all say the same thing then you may just ask that group of people the question but you can say it came from a study of the whole population. This leads people to think that more people than actually do in the general population believe/ agree with the question you are asking them. Another way statistics can be misleading is if you ask a bias leading question. This would be a question were you are suggesting the answer before the respondent has a chance to answer the question. "If you say don't you think" before the question this would be a bias leading question. Also, you can skew what the average of something actually is by using the arithmetic mean, mode and average which are all technically the average. Lastly, you can use volume instead of bars to make it look like there was a huge increase or decrease in something when there really wasn't. Statistical results published by journalists in the media and scientific journals can be immensely misleading. We can sometimes trusty these
11/5/14
1st
Math Extra Credit Essay In the graphs provided it is very obvious that the author in the second graph has lied. The graphs were exactly the same besides the fact that in the second graph the author added a break in the graph which completely skews the data. Instead of looking like a modest profit growth like the first graph it looks like a huge increase in profit because the whole graph is not shown. The scale on the first graph is bigger so it doesn't look like as big as a jump compared to the second graph. Statistics can be used in many different ways to deliberately mislead the audience. One major way is by adding a break in a graph as seen above. Another way to deliberately mislead the audience is to change the scale of the graph which makes it looks like it went up or down more or less than it really did. Also, another way the audience can be deliberately fooled is by using a bad sample of data. If you ask people questions and you know they will all say the same thing then you may just ask that group of people the question but you can say it came from a study of the whole population. This leads people to think that more people than actually do in the general population believe/ agree with the question you are asking them. Another way statistics can be misleading is if you ask a bias leading question. This would be a question were you are suggesting the answer before the respondent has a chance to answer the question. "If you say don't you think" before the question this would be a bias leading question. Also, you can skew what the average of something actually is by using the arithmetic mean, mode and average which are all technically the average. Lastly, you can use volume instead of bars to make it look like there was a huge increase or decrease in something when there really wasn't. Statistical results published by journalists in the media and scientific journals can be immensely misleading. We can sometimes trusty these