The truth about Thanksgiving Essay Example For Students

The truth about Thanksgiving Essay The truth behind the tradition is surprising. Thanksgiving and the Pilgrims seem to go together, but the truth is, the Pilgrims never held an autumnal Thanksgiving feast. However the Pilgrims did have a feast in 1621, after their first harvest, and it is this feast, which people often refer to as The First Thanksgiving. This feast was never repeated, though, so it cant be called the beginning of a tradition, nor was it termed by the colonists or Pilgrims a Thanksgiving Feast. In fact, a day of thanksgiving was a day of prayer and fasting, and would have been held any time that they felt an extra day of thanks was called for. We will write a custom essay on The truth about Thanksgiving specifically for you for only $16.38 $13.9/page Order now Nevertheless, the 1621 feast has become a model that we think of for our own Thanksgiving. The Pilgrims were not the first people to have a celebration of this kind. Many other civilizations held festivals to celebrate the harvest. The ancient Greeks and Romans prayed to the gods and goddesses of the harvest, and also originated the idea of the cornucopiathe horn of plenty. The Jews celebrate the holiday Sukkot, which honors the awards of the harvest, and the Chinese enjoy the celebration of the Harvest Moon. Even native New Yorkers commemorate the harvest long before Thanksgiving arrives. Pumpkins, apples and corn are abundant in the open-air markets of the city beginning in late September. The autumn of 1621 yielded a plentiful harvest and the Pilgrims, gathered together with the Massasoit Indians to reap the awards of hard work.Celebrating Thanksgiving is like celebrating an even that includes the dead of over 11,000 Wampanoag Indians died due to illnesses that they contracted from white settlers. The truth of the matter is, when the Pilgrims arrived, they found an abandoned Wampanoag village and moved right in. In 1618, a massive epidemic of an unknown disease left by English explorers swept across Wampanoag country and decimated many of the villages. This epidemic caused the death of ten to thirty percent of the total population and all but a few of the 2,000 people of the village of Patuxet. When the Pilgrims arrived in 1620, they landed at Patuxet with no idea of what had occurred. At this point, there were only about 2,000 members left in the Wampanoag tribe, down from 12,000 in 1600. Despite the incredible losses to his people, Wampanoag leader Massasoit and 90 of his men sat down for a harvest celebration offered by the white men. For three days the Wampanoag and Pilgrims feasted on deer, wild turkey, fish, beans, squash, corn and other foods native to North America. Although the celebration was good-natured, this event truly signifies the beginning of a drastic decline of native culture and Thanksgiving would be more fittingly observed as a day of mourning rather than a celebration. In the years that followed, skirmishes occurred and more Native Americans were killed. In 1637, English soldiers massacred 700 Pequot men, women and children as an example of the English way of war, yet we still celebrate Thanksgiving as a joyful event. So, as we sit down for our Thanksgiving dinner, let us consider the words of Frank James in his 1970 speech: Today is a time of celebrating for you but it is not a time of celebrating for me. It is with a heavy heart that I look back upon what happened to my people. When the Pilgrims arrived, we, the Wampanoags, welcomed them with open arms, little knowing that it was the beginning of the end. Bibliography: .

Hypothesis Test Example of Calculating Probability

Hypothesis Test Example of Calculating Probability An important part of inferential statistics is hypothesis testing. As with learning anything related to mathematics, it is helpful to work through several examples. The following examines an example of a hypothesis test, and calculates the probability of type I and type II errors. We will assume that the simple conditions hold. More specifically we will assume that we have a simple random sample from a population that is either normally distributed or has a large enough sample size that we can apply the central limit theorem. We will also assume that we know the population standard deviation. Statement of the Problem A bag of potato chips is packaged by weight. A total of nine bags are purchased, weighed and the mean weight of these nine bags is 10.5 ounces. Suppose that the standard deviation of the population of all such bags of chips is 0.6 ounces. The stated weight on all packages is 11 ounces. Set a level of significance at 0.01. Question 1 Does the sample support the hypothesis that true population mean is less than 11 ounces? We have a lower tailed test. This is seen by the statement of our null and alternative hypotheses: H0 : ÃŽ ¼11.Ha : ÃŽ ¼ 11. The test statistic is calculated by the formula z (x-bar - ÃŽ ¼0)/(ÏÆ'/√n) (10.5 - 11)/(0.6/√ 9) -0.5/0.2 -2.5. We now need to determine how likely this value of z is due to chance alone. By using a table of z-scores we see that the probability that z is less than or equal to -2.5 is 0.0062. Since this p-value is less than the significance level, we reject the null hypothesis and accept the alternative hypothesis. The mean weight of all bags of chips is less than 11 ounces. Question 2 What is the probability of a type I error? A type I error occurs when we reject a null hypothesis that is true. The probability of such an error is equal to the significance level. In this case, we have a level of significance equal to 0.01, thus this is the probability of a type I error. Question 3 If the population mean is actually 10.75 ounces, what is the probability of a Type II error? We begin by reformulating our decision rule in terms of the sample mean. For a significance level of 0.01, we reject the null hypothesis when z -2.33. By plugging this value into the formula for the test statistics, we reject the null hypothesis when (x-bar – 11)/(0.6/√ 9) -2.33. Equivalently we reject the null hypothesis when 11 – 2.33(0.2) x-bar, or when x-bar is less than 10.534. We fail to reject the null hypothesis for x-bar greater than or equal to 10.534. If the true population mean is 10.75, then the probability that x-bar is greater than or equal to 10.534 is equivalent to the probability that z is greater than or equal to -0.22. This probability, which is the probability of a type II error, is equal to 0.587.