Chi-Square Testing
Persuasion is the name of the game. Many times, many infomercials are scripted to make what is being sold stimulating, interesting, and full of false hope and promises. Furthermore, the individuals advertising and selling the product cause you to feel that you are missing out on a gem if you do not have the product in your home. In addition, many infomercials are designed to be believable and make people feel the items work miracles and then when you purchase them and get them home they do not perform as intended. A lot of times “seeing is believing” especially for many infomercials and the reason why people purchase the item because they are in amazement of what they just saw. At times that statement can be true, because many infomercials
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We are able to utilize this type of testing to estimate the likelihood that the results and data about infomercial products observed occurred by chance. According to Dr. Mirabella (2011), if the observations are not equivalent to the expectations, then that is the time to observe and track more directly and gather more information. Chi-Square testing is one of the simplest techniques to use and the most applicable (Mirabella 2011). Therefore, we use hypothesis testing to see if we should dig further for more additional information. Even more, we utilize this test to show a comparison of observed and expected values. Another factor that would help convince me that the claims might be true is if they used cross tabulation, as it can be beneficial in reviewing customer feedback and also you are able to find correlations between responses to varying questions. As stated by Mirabella (2011, p. 6-2) states that, “Statistics tables tell us the magic number above which the Chi Square statistic is considered significant. If our observed values exactly matched the expected values…, the computed Chi Square statistic would be zero, which is as small as it gets. So, the larger the value the more that the results veer from the expected
We are able to utilize this type of testing to estimate the likelihood that the results and data about infomercial products observed occurred by chance. According to Dr. Mirabella (2011), if the observations are not equivalent to the expectations, then that is the time to observe and track more directly and gather more information. Chi-Square testing is one of the simplest techniques to use and the most applicable (Mirabella 2011). Therefore, we use hypothesis testing to see if we should dig further for more additional information. Even more, we utilize this test to show a comparison of observed and expected values. Another factor that would help convince me that the claims might be true is if they used cross tabulation, as it can be beneficial in reviewing customer feedback and also you are able to find correlations between responses to varying questions. As stated by Mirabella (2011, p. 6-2) states that, “Statistics tables tell us the magic number above which the Chi Square statistic is considered significant. If our observed values exactly matched the expected values…, the computed Chi Square statistic would be zero, which is as small as it gets. So, the larger the value the more that the results veer from the expected