A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. It is mostly used when the data sets, like the data set recorded as the outcome from flipping a coin 100 times, would follow a normal distribution and may have unknown variances. A t-test is used as a hypothesis testing tool, which allows testing of an assumption applicable to a population. Essentially, a t-test allows us to compare the average values of the two data sets and determine if they came from the same population. Mathematically, the t-test takes a sample from each of the two sets and establishes the problem statement by assuming a null hypothesis that the two means are equal. Based on the applicable formulas, certain values are calculated and compared against the standard values, and the assumed null hypothesis is accepted or rejected accordingly.
CD ComputaBio provides a single population t-test service, which tests whether the difference between a sample average and a known population average is significant. When the population distribution is a normal distribution, such as the population standard deviation is unknown and the sample size is less than 30, then the deviation statistics of the sample mean and the population mean are distributed at t.
CD ComputaBio provides a two-population t test, which is to test whether the difference between the average of two samples and the population represented by each is significant. The two-population t test is divided into two situations. One is the independent sample t test (there is no correlation between the experimental treatment groups, that is, the independent sample). This test is used to test the results obtained by the two groups of unrelated samples. The difference of data; the other is a paired-sample t test, which is used to test the data obtained by two matched groups of subjects or the difference of data obtained by the same group of subjects under different conditions. The sample composed of these two situations is the relevant sample.
1. Find the standard score of t distribution.
2. Determine the confidence level.
Confidence level refers to the overall statistics contained in the confidence interval. How much information this statement has, it helps us to point out how wide the confidence interval is.
3. Find the upper and lower bounds of the confidence interval.
|Project name||T-test analysis|
|Sample requirements||(1) Knowing a population mean;
(2) A sample mean and standard deviation of the sample can be obtained;
(3) The sample comes from a normal or approximately normal population.
|Screening cycle||Decide according to your needs.|
|Deliverables||We provide you with raw data and analysis service.|
CD ComputaBio' T-test analysis can significantly reduce the cost and labor of the subsequent experiments. T-test analysis is a personalized and customized innovative scientific research service. Each project needs to be evaluated before the corresponding analysis plan and price can be determined. If you want to know more about service prices or technical details, please feel free to contact us.