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One-Sample t Test in R (R Tutorial 4.1) Video Lecture | Mastering R Programming: For Data Science and Analytics - Database Management
FAQs on One-Sample t Test in R (R Tutorial 4.1) Video Lecture - Mastering R Programming: For Data Science and Analytics - Database Management
| 1. What is a one-sample t test in R? |  |
Ans. A one-sample t test in R is a statistical test used to determine if the mean of a single group differs significantly from a hypothesized value. It is commonly used to compare the mean of a sample to a known population mean or a specific value. The t test calculates the t-statistic and p-value to assess the statistical significance of the difference.
| 2. How can I perform a one-sample t test in R? | |
Ans. To perform a one-sample t test in R, you can use the t.test() function. First, you need to specify the data you want to test. Then, you can provide the hypothesized value for comparison. R will calculate the t-statistic, degrees of freedom, and p-value automatically. You can interpret the results to determine if the mean is significantly different from the hypothesized value.
| 3. What is the null hypothesis in a one-sample t test? | |
Ans. In a one-sample t test, the null hypothesis states that there is no significant difference between the mean of the sample and the hypothesized value. It assumes that any observed difference is due to random chance. The alternative hypothesis, on the other hand, suggests that there is a significant difference between the mean and the hypothesized value.
| 4. How do I interpret the p-value in a one-sample t test? | |
Ans. The p-value in a one-sample t test represents the probability of obtaining a sample mean as extreme as the observed one, assuming the null hypothesis is true. If the p-value is less than a chosen significance level (e.g., 0.05), it indicates that the observed mean is significantly different from the hypothesized value. A smaller p-value suggests stronger evidence against the null hypothesis.
| 5. Can a one-sample t test be used for non-normal data? | |
Ans. The one-sample t test assumes that the data are normally distributed. However, if the sample size is large enough (typically considered as n > 30), the test can be robust to violations of normality. If the data are severely non-normal or the sample size is small, it is advisable to use non-parametric tests like the Wilcoxon signed-rank test instead.
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Nov 24, 2024 Last updated |
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