Z-test & T-test Assessment Test

The T test is used for comparing the means of two populations when the standard deviation is unknown. It is a statistical test that determines if the difference between the means of two groups is statistically significant. The T test calculates the T statistic by comparing the means and standard deviations of the two groups, and then determines the probability of obtaining the observed difference if the null hypothesis (no difference between the means) is true. If the probability is below a predetermined significance level, it is concluded that there is a significant difference between the means of the two populations.

When you know the population's standard deviation, you use the Z test. The Z test is a statistical test that is used to determine whether the mean of a sample is significantly different from a known population mean when the population standard deviation is known. It is based on the standard normal distribution and allows for hypothesis testing and calculating p-values. The Z test is commonly used in research and statistical analysis to make inferences about population means.

When you know the population's standard deviation, you use a Z test. A Z test is a statistical test that is used to determine whether the means of two populations are significantly different from each other when the population standard deviation is known. It compares the observed data to the expected data under the null hypothesis and calculates a Z score, which is then compared to a critical value to determine the significance of the result.

The One-Sample z-test is used when we want to determine if the difference between a sample mean and the population mean is statistically significant. This test calculates the z-score, which measures how many standard deviations the sample mean is away from the population mean. By comparing the calculated z-score to a critical value, we can determine if the difference is large enough to be statistically significant.

A T test is used to test a hypothesis by comparing the means of two groups and determining if there is a significant difference between them. It calculates the t-value, which is then compared to a critical value to determine if the results are statistically significant. The T test is commonly used when the sample size is small and the population standard deviation is unknown. It helps researchers make inferences about the population based on the sample data. The F test, on the other hand, is used to compare the variances of two or more groups. Litmus test and theory are not calculations used to test a hypothesis.

If the mean change score is not significantly different from zero, it means that the observed change is not statistically significant. This suggests that there is no evidence to support the occurrence of a significant change. Therefore, the correct answer is "No significant change occurred".

The T test is used when there is a limited sample size. This is because the T test is specifically designed to analyze small sample sizes, where the population standard deviation is unknown. It is a statistical test that compares the means of two groups to determine if there is a significant difference between them. In situations where there is a large sample size, other tests like the Z test or chi-square test may be more appropriate.

The P value is a statistical measure that indicates the likelihood of obtaining a sample result by random chance. It is used in hypothesis testing to determine the significance of the results. A smaller P value suggests that the sample result is less likely to occur by random chance, indicating stronger evidence against the null hypothesis. Therefore, the P value is the correct answer as it directly relates to the likelihood of a sample result occurring by random chance.

If your T-Score is above 50, it means that your score is higher than the average. Therefore, the correct answer is "Above average".

The Z test is a statistical calculation that can be used to compare population means to a sample. It is commonly used when the sample size is large and the population standard deviation is known. The Z test calculates the Z score, which measures how many standard deviations the sample mean is away from the population mean. By comparing the Z score to a critical value, we can determine if the sample mean is significantly different from the population mean.