Hillary Clinton Argument Analysis

My prediction about Hillary Clinton becoming the president was wrong. Donald Trump won the election and defeated Clinton. Clinton did not get enough votes as predicted by poll data. Trump got votes from Clinton’s supporters that did not vote her. The predictive models of politics can be improved by not being biased when making prediction, looking at more data, and viewing the viewpoint of both sides of arguments. Clinton did not get plenty votes as expected by polling data. My prediction that Clinton would get more votes than Trump was wrong as polling data was not accurate. According to the result of 2016 presidential election on the article “Presidential results” on the website www.cnn.com, “Trump had 306 electoral votes and 62,792,756 popular votes, and Clinton had 232 electoral votes and 65,431,654 popular votes.” Clinton lost votes and Trump gained her votes, which resulted in her defeat. Polling data that predicted Clinton would …show more content…
Bias should not be a consideration while making a prediction as it does not give accurate prediction. More data has to be analyzed before making final prediction to make sure all the data correct data is taken into consideration when making prediction. Both sides of the arguments have to be viewed to make sure that prediction covers points from both sides and not just from one side. I have paid more attention to looking at more data, and I weigh looking at both sides of arguments correctly because I looked at both sides equally when I made my prediction.
Clinton did not get enough votes as predicted by polls, as poll data was wrong. Clinton lost voters and her voters voted Trump due to her e-mail scandal. To improve the predictive models of politics bias should not be a factor, lots of data has to viewed, and both sides of the arguments needs equal