REPLY 5-1 OlMa (100 words and 1 reference)

Within the area of statistics, there is a certain type of hypothesis test, referred to as t-tests, that are used to “evaluate the means of one or two populations”, and may be utilized to examine one-sample, independent two-samples, or dependent samples tests of values (JMP Statistical Discovery, 2022). While the options of sample tests may seem flexible at adding in variables, it is critical to understand that t-tests cannot be used in samples of over two groups. The tools of t-tests must be adequately executed for accurate results of a study, and demand the researcher’s hypothesis to be defined as well as highlighting the adequate possibility that there is a risk of a “faulty conclusion” (JMP Statistical Discovery, 2022). From this point, researchers must assess test statistics within their data collection and analyze it with a “theoretical value from a t-distribution”, which may either refuse or may be inadequate at refuting one’s null hypothesis (JMP Statistical Discovery, 2022). Some of the advantages of using t-tests include the ease of obtaining evaluations from samples, analyzing the output of a sample, organizing generalized results, comprehending source data, and demonstrating critical information regarding the sample populations involved (Admin., 2021). Some of the disadvantages of using t-tests include the challenge of comparisons between more than two variables, carry-over effects, setting and dissecting differences between values, as well as a loss of freedom for degrees (Admin., 2021). An example of t-tests in real life would be measuring growth samples of two populations of bean plants, using the variables of different soils to determine if growth is better in one or another type of soil, and locating the mean measurements for the conclusion of data.

REPLY 5-1 XiAv (100 words and 1 reference)

The t-test allows for a comparison of two means, Witte & Witte (2017) emphasize that the t-test is not designed for multiple comparisons across all pairs of observed means which can potentially be a weakness. The specifics of the test can be a strength as there is a focus on two means, the data, population, or factors gathered are catered around the two. A real-world example may be the effectiveness of natural remedies versus pharmaceutical medicine. For example, if someone is experiencing a migraine, what would be the effect to decrease the symptoms rapidly? If using a natural remedy, the effectiveness of peppermint and a cold towel may decrease some symptoms faster than when compared to taking pain relieving medication. It is difficult to measure the exact effectiveness as the migraine and level of pain may not be consistent across the board, different factors, including pain tolerance, sensitivity to light, level of pain, water and rest levels, and other factors may interfere with measuring the effectiveness and consistency.

REPLY 5-2 AsSh (100 words and 1 reference)

T-test for correlated groups and the T-test for single samples are alike and different because T-tests are used for correlated groups, when two groups are given for one sample, when only one group on one sample is given.  

 

One sample t-test compares one sample mean to the hypothesis value and t-test for correlated groups compare the 2 sample means to the null hypothesis value.  

 

Test statistics for both tests are different. One sample t-test we calculated different data with sample mean and population mean divided by standard error but in t-test for correlated test we calculated mean difference between 2 groups divided by standard error of different.  

 

 

Here both tests are different. 

t test for single sample is used to test single mean. but t test for correlated or paired t test is used to find the homogeneity of two groups. here the two examples

a) sample sizes should be same 

b) both sample groups should be dependent. 

 

Example for single mean: 

What if a professor at a college claim that an average student at the college studies 6 hours per day during weekends and he wants to test the truth of this claim. 

The statistical methodology for this purpose requires that he begin by first specifying the hypothesis to be tested. 

 

In this case, the null hypothesis would be H0: μ = 6, which essentially states that mean hours of study per day is no different from 6 hours. And the alternative hypothesis is, H1: μ ≠ which is negation of the professor's claim. 

 

Example for paired t test: 

Twins (genetic match) are assigned to take either a drug or a placebo (for each pair of twins, one takes the drug the other takes the placebo) to see if would help increase their intelligence.

NIH National Library of Medicine. (2022). The Differences and Similarities Between Two-Sample T-Test and Paired T-Test.