To use the data analysis version found in the Real Statistics Resource Pack, enter Ctrl-m and select T Tests and Non-parametric Equivalents from the menu. A dialog box will appear (as in Figure 3 of Two Sample t Test: Unequal Variances).

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Paired-samples t test (dependent t test) This is used to compare the means of two variables for a single group. The procedure computes the differences between values of the two variables for each case and tests whether the average differs from zero.

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T-Test Assumptions The t-test is a parametric statistic and perhaps one of the simplest analyses used in dissertation and thesis research. Prior to using the t-test, you must make sure that your data does not violate any of the three assumptions underlying the t-test: The scores in your data represent a random sample from the population under.

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In our first research example, you would rely on MANOVA (to see if women of different nationalities experience different stress levels at the beginning, at three months, at six months, and at nine months of pregnancy); and in our second example, you would use an independent samples t-test (to see if interest in Greek tourism differs between Germans and Britons).

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Data Analysis The t-test is used to compare the values of the means from two samples and test whether it is likely that the samples are from populations having different mean values. When two samples are taken from the same population it is very unlikely that the means of the two samples will be identical.

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Now that we know what a one-sample t-test is used for, we can now calculate a one-sample t-test in Excel. To begin, open your data in Excel. If you don’t have a dataset, download the example dataset here. In the example dataset, we are comparing the test grades of a class to the chosen value of 80. This will determine whether the students.

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As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test). At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected.

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Chapter 5: Comparing two means using the t-test 4 Using the T-Test for INDEPEDENT MEANS The t-test is a powerful statistic that enables you to determine that the differences obtained between two groups is statistically significant. When two groups are INDEPENDENT of each other; it means that the sample drawn came from two populations.

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If normality can safely be assumed, then the one-sample t-test is the best choice for assessing whether the measure of central tendency, the mean, is different from a hypothesized value. On the other hand, if normality is not valid, one of the nonparametric test s, such as the Wilcoxon Signed Rank test or the quantile test, can be applied.

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Writing a thesis also assesses your capability in employing an initiative and in time management. Next, a thesis provides opportunities to you which take your writing to the next level. If your thesis is of excelling academic standard, it could be published in journals, be it national or international.

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Dissertation word counts vary between subjects, but will typically be between 9000 and 18000 words. Being clear about your word count is vital to the planning process as it will give you an indication of the amount of time you will need to spend researching and writing your dissertation and the level of detail you will be expected to go into in each chapter.

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Independent-samples t-test using R, Excel and RStudio (page 2) On the previous page you learnt about the type of research where an independent-samples t-test can be used and the critical assumptions of the independent-samples t-test that your study design, variables and data must meet in order for the independent-samples t-test to be the correct statistical test for your analysis.

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