The T-TEST 1.0 INTRODUCTION The t-test was developed by W. S. Gossett‚ a statistician employed at the Guinness brewery. However‚ because the brewery did not allow employees to publish their research‚ Gossett’s work on the t-test appears under the name "Student". The t-test is sometimes referred to as "Student’s t-test." Gossett was a chemist and was responsible for developing procedures for ensuring the similarity of batches of Guinness. The t-test was developed as a way of measuring how closely
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assumptions for the paired sample t-test were evaluated and no violations were noted. Results from the paired sample t-test revealed statistically significant differences (p <= .05) in student competency self-assessment between the pretest and the posttest‚ and the posttest and the retrospective test on all 19 competencies (Table 2). Cohen’s effect size values ranging from 0.51 to 2.30 suggested moderate or high practical significance. These findings indicate that students perceive significant progress
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40 hours per week. (mean=45.63‚ SD=10.63). The difference was significant (t=20.48‚ p<.001)The 95% confidence interval of the difference was between 5.09 and 6.17. Group Statistics Respondent’s sex N Mean Std. Deviation Std. Error Mean Hours per day watching TV Male 382 3.01 2.648 .135 Female 524 3.00 2.497 .109 Independent Samples Test Levene’s Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval
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Assessing T-tests To clearly identify what a t-test accomplishes in descriptive statistics it is imperative to understand what a t-test represents. A “t-test is a parametric statistical test for comparing the means of two independent samples” (Plichta & Kelvin‚ 2013‚ p. 464). Gosset developed the t-test for use in quality control at the Guinness Brewery and published his works under the pen name “Student” (Plichta & Kelvin‚ 2013). T-tests use assumptions related to the underlying
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between Traditional and Non-traditional Students in a Statistics Based Classroom Abstract This report examines the differences between traditional and non-traditional students in terms of three aspects; anxiety towards statistics‚ attitude towards statistics and computer self-efficacy. A review of literature was conducted and hypotheses were formed about the three aspects. The three hypotheses tested were and what was expected to be found was; traditional students will score lower on the statistics anxiety
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The T-Distribution and T-Test “In probability and statistics‚ Student ’s t-distribution (or simply the t-distribution) is a continuous probability distribution that arises when estimating the mean of a normally distributed population in situations where the sample size is small” (Narasimhan ‚ 1996). Similar to the normal distribution‚ the t-distribution is symmetric and bell-shaped‚ but has heavier tails‚ meaning that it is more likely to produce values far from its mean. This makes the t-distribution
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Z –Test: a statistical test used for inference (inference – is the act or process of deriving logical conclusions from evidence‚ statements‚ ideas‚ etc. known or assumed to be true) which determines if the difference between a sample mean and the population mean is large enough to be statistically significant‚ that is‚ if it is unlikely to have occurred by chance. The Z – test is used primarily with its standardized testing to determine if the test scores of a particular sample of test takers are
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Two-Sample t-Tests Connie Tyrone Walden University August 24‚ 2013 Two Sample t-Tests With this assignment‚ we are told about Martha. Martha wants to see if her relaxing technique which involves visualization will be able to assist people suffering with mild insomnia to fall asleep faster. She randomly selects 20 insomnia patients to participate in her research. She assigns 10 from the group to participate in visualization therapy and 10 from the group receives no treatment. Martha then
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Average of the pre-test‚ the test‚ the post-test and the learning gains for the 2nd experimentation Pre-test Post-test1 Post-test2 Learning gain(pre-test/pots-test1) Learning gain(pre-test/post-test2) Conversation only/Full system 0.615 2.615 5 2 4.153 As we can see there is a difference between the learning gain after using simulation only and the learning
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- Snedecor distribution ( After R.A. Fisher and George W. Snedecor)(2) which arises in the testing of whether two observed samples have the same variance. (1) Note that three of the most important distributions (namely the normal distribution‚ the t distribution‚ and the chi-square distribution) may be seen as special cases of the F distribution: (3) Example: We want to measure the monthly sales volume from Microsoft and Apple. We collect data for a year ( 12 months). We calculate the variance
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