英语论文网

othesis tests we want toknow whether they are indicative of population differences. The results canonly be inferred on the population from which it is drawn it would not be validotherwise.
Details of sampling methods were found in Bland (2000). Toaccomplish the required objectives, the sample has to be representative of thedefined population. It would also be more accurate if the sample is stratifiedby known factors like gender and age. This means that, for example, theproportion of males in the sample is the same as the proportion in thepopulation.
Sample size is another consideration. In this case it is 30.Whether this is adequate for the hypotheses being tested is examined below.
Hypothesis 1: Male children are taller than femalechildren.
Swift (2001) gives a very readable account of the hypothesistesting process and the structure of the test.
The first step is to set up the hypotheses:
The Null hypothesis is that there is no difference in heightbetween male children and female children.
If the alternative was as Coolican describes it as "wedo not predict in which direction the results will go then it would have beena two-tailed test. In this case the alternative is that males are taller it istherefore a specific direction and so a one-tailed test is required.
To test the hypothesis we need to set up a test statisticand then either match it against a pre-determined critical value or calculatethe probability of achieving the sample value based on the assumption that thenull hypothesis is true.
The most commonly used significance level is 0.05. Accordingto Swift (2001) the significance level must be decided before the data isknown. This is to stop researchers adjusting the significance level to get theresult that they want rather than accepting or rejecting objectively.
If the test statistic probability is less than 0.05 we wouldreject the null hypothesis that there is no difference between males andfemales in favour of males being heavier on the one sided basis.
However it is possible for the test statistic to be in therejection zone when in fact the null hypothesis is true. This is called a TypeI error.
It is also possible for the test statistic to be in theacceptance zone when the alternative hypothesis is true (in other words thenull hypothesis is false). This is called a Type II error. Power is 1 -probability of a Type II error and is therefore the probability of correctlyrejecting a false null hypothesis. Whereas the Type I error is set at thedesired level, the Type II error depends on the actual value of the alternativehypothesis.
Test method
The data for gender is categorical and for height the datais ratio. The sample is effectively split into 2 sub-sets for male and female.
Most books give the independent samples t-test as the mainmethod for testing this hypothesis e.g. Curwin, et al (2001), Swift (2001).
Bland (2000) states that in order to use this test thesamples must both be from a normal populations and additionally thedistributions must have the same variance. Bland also suggests modifications tothe test when the variances cannot be assumed to be the same. Programs likeSPSS will calculate both for equal and non-equal variances. SPSS also gives atest for equality of variances.
When the assumptions of normality and independence are metthen the t-test is the best test according to Bland because it has本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。