The Quiet American essays

The Quiet American essays In Graham Greene's The Quiet American, Greene uses the characters Thomas Fowler and Alden Pyle to represent a greater picture. In the interactions among these characters, he is simplifying the situation in Vietnam into a personal model to be viewed. Graham Greene developed the attitudes and personalities of his characters almost to be a condensed legend of the countries they represented. In their actions, and opinions formed on them by others, was a reflection of the general feeling overall in Vietnam. Alden Pyle is the title quiet American sent to Vietnam with orders. Seemingly he is quiet because he is the innocent, neutral party coming to aid by selling plastic. He has a good reputation, and is very curt and proper. Naive is best to superficially describe his demeanor; he is only trying to help. But ignorance is probably better to this character for he does not realize the destruction he is causing and does not realize that he is more meddlesome than helpful. And that is exactly what Graham Greene is trying to portray this character's representation of the United States. Pyle as an individual reflects America as a whole as they were seen as ignorantly trying to interfere in Vietnam, being neutral. But actually harming in trying to help Pyle as a character and as the symbol of his country was portrayed of promising the Vietnamese things, trying to forge for them a new life they did not need nor understand. This is seen with his relationship with Phuong as he woos her away from Thomas Fowler with promises of skyscrapers and the Statue of Liberty. Phuong, can be seen as the innocent country Vietnam whose promising lands pose the stage for a war between the politics of greedier forces. Phuong and the majority of Vietnam, the peasants, know nothing but their simple existence. They farm their rice paddies; they sustain themselves- that is all they know. No matter who wins the war, how will it affect most ...

How to Conduct a Hypothesis Test in Statistics

How to Conduct a Hypothesis Test in Statistics The idea of hypothesis testing is relatively straightforward. In various studies, we observe certain events. We must ask, is the event due to chance alone, or is there some cause that we should be looking for? We need to have a way to differentiate between events that easily occur by chance and those that are highly unlikely to occur randomly. Such a method should be streamlined and well defined so that others can replicate our statistical experiments. There are a few different methods used to conduct hypothesis tests. One of these methods is known as the traditional method, and another involves what is known as a p-value. The steps of these two most common methods are identical up to a point, then diverge slightly. Both the traditional method for hypothesis testing and the p-value method are outlined below. The Traditional Method The traditional method is as follows: Begin by stating the claim or hypothesis that is being tested. Also, form a statement for the case that the hypothesis is false.Express both of the statements from the first step in mathematical symbols. These statements will use symbols such as inequalities and equals signs.Identify which of the two symbolic statements does not have equality in it. This could simply be a not equals sign, but could also be an is less than sign ( ). The statement containing inequality is called the alternative hypothesis and is denoted H1 or Ha.The statement from the first step that makes the statement that a parameter equals a particular value is called the null hypothesis, denoted H0.Choose which significance level that we want. A significance level is typically denoted by the Greek letter alpha. Here we should consider Type I errors. A Type I error occurs when we reject a null hypothesis that is actually true. If we are very concerned about this possibility occurring, then our value for alpha shoul d be small. There is a bit of a trade-off here. The smaller the alpha, the most costly the experiment. The values 0.05 and 0.01 are common values used for alpha, but any positive number between 0 and 0.50 could be used for a significance level. Determine which statistic and distribution we should use. The type of distribution is dictated by features of the data. Common distributions include z score, t score, and chi-squared.Find the test statistic and critical value for this statistic. Here we will have to consider if we are conducting a two-tailed test (typically when the alternative hypothesis contains a â€Å"is not equal to† symbol, or a one-tailed test (typically used when an inequality is involved in the statement of the alternative hypothesis).From the type of distribution, confidence level, critical value, and test statistic we sketch a graph.If the test statistic is in our critical region, then we must reject the null hypothesis. The alternative hypothesis stands. If the test statistic is not in our critical region, then we fail to reject the null hypothesis. This does not prove that the null hypothesis is true, but gives a way to quantify how likely it is to be true.We now state the results of the hypothesi s test in such a way that the original claim is addressed. The p-Value Method The p-value method is nearly identical to the traditional method. The first six steps are the same. For step seven we find the test statistic and p-value. We then reject the null hypothesis if the p-value is less than or equal to alpha. We fail to reject the null hypothesis if the p-value is greater than alpha. We then wrap up the test as before, by clearly stating the results.