The formula to calculate the test statistic comparing two population means is, Z= ( x - y )/√(σx2/n1 + σy2/n2). In order to calculate the statistic, we must calculate the sample means ( x and y ) and sample standard deviations (σx and σy) for each sample separately.
A test statistic measures the degree of agreement between a sample of data and the null hypothesis. Its observed value changes randomly from one random sample to a different sample. A test statistic contains information about the data that is relevant for deciding whether to reject the null hypothesis.
Purpose: Test if two population means are equal. The two-sample t-test (Snedecor and Cochran, 1989) is used to determine if two population means are equal. A common application is to test if a new process or treatment is superior to a current process or treatment.
The value for z is calculated by subtracting the value of the average daily return selected for the test, or 1% in this case, from the observed average of the samples. Next, divide the resulting value by the standard deviation divided by the square root of the number of observed values.
A statistic (singular) or sample statistic is any quantity computed from values in a sample which is considered for a statistical purpose. Statistical purposes include estimating a population parameter, describing a sample, or evaluating a hypothesis. The average (or mean) of sample values is a statistic.
A test statistic is used in a hypothesis test when you are deciding to support or reject the null hypothesis. The test statistic takes your data from an experiment or survey and compares your results to the results you would expect from the null hypothesis.Nov 3, 2013
The test statistic for a two-sample independent t-test is calculated by taking the difference in the two sample means and dividing by either the pooled or unpooled estimated standard error. The estimated standard error is an aggregate measure of the amount of variation in both groups.
Higher values of the t-value, also called t-score, indicate that a large difference exists between the two sample sets. The smaller the t-value, the more similarity exists between the two sample sets. A large t-score indicates that the groups are different. A small t-score indicates that the groups are similar.
The p-value is the probability that the difference between the sample means is at least as large as what has been observed, under the assumption that the population means are equal.
To find the p-value, we can first locate the value -0.84 in the z table: Since we're conducting a two-tailed test, we can then multiply this value by 2. What is this? So our final p-value is: 0.2005 * 2 = 0.401.May 7, 2021
What is a T Statistic? The T Statistic is used in a T test when you are deciding if you should support or reject the null hypothesis. It's very similar to a Z-score and you use it in the same way: find a cut off point, find your t score, and compare the two.Sep 25, 2013