One sample t-test in R - ALL IN ONE (Assumptions, Calculation, Interpretation, Reporting) скачать в хорошем качестве

One sample t-test in R - ALL IN ONE (Assumptions, Calculation, Interpretation, Reporting)

// One sample t-test in R - ALL IN ONE (Assumptions, Calculation, Interpretation, Reporting) // This video will help you in conducting a one sample t-test, interpreting and reporting its results. Please don't forget that an a priori sample size calculation is usually required and that the assumption of normal distribution of the test variable has to be checked. The video consists of the following four parts: 1) Testing the assumption of normal distribution of the test variable in R. I'll show the Shapiro-Wilk-test with a reminder to be cautious and rather opt for a histogram or a q-q-plot. 2) Calculation of the one sample t-test in R, including the effect size Cohen's d, in case of a significant difference between the sample mean and a hypothesized mean (i.e. population mean). 3) The interpretation of the results, especially the effect size Cohen's d, in case of a significant test result. 4) Reporting of the results. Be aware that research field-specific standards may apply. The reporting shown is usually sufficient. Calculating the required sample size: ============================== 🎥    • Chi Square Goodness of Fit - calculate req...   📚 Sources: ========== Lantz, B. (2013). The large sample size fallacy. Scandinavian journal of caring sciences, 27(2), 487-492. Basic information on the one sample t-test ================================== The one sample t-test is a statistical test that is used to determine whether there is a significant difference between the mean of a sample and a known population or hypothesized mean. The p-value of the one sample t-test is the probability that the difference between the sample mean and the population mean is due to chance. If the p-value is below a certain threshold (usually 0.05), you can reject the null hypothesis and conclude that there is a significant difference between your sample mean and the population or hypothesized mean. The one sample t-test is commonly used in a variety of fields, including psychology, economics, and biology, to test hypotheses about population means. If the requirements of normal distribution and a test variable on interval or ratio level is not met, you should fall back on the one sample Wilcoxon-test. ⏰ Timestamps: ============== 0:00 Introduction 0:15 Example 0:23 Requirements 0:30 Normal distribution 1:58 One sample t-test (one and two-sided) 2:49 Interpreting the results of the one sample-test 3:32 Calculating the effect size Cohen's d 4:05 Classifying Cohen's d 4:37 Reporting the results If you have any questions or suggestions regarding the one sample t-test, please use the comment function. Thumbs up or down to decide if you found the video helpful. #statisticampc #useR #statorials Support channel? 🙌🏼 =================== Paypal donation: https://www.paypal.com/paypalme/Bjoer... Amazon affiliate link: https://amzn.to/49BqP5H