2 Sample T Test
Not only will we see how to conduct a hypothesis test about the difference of two population means, we will also construct a confidence interval for this difference. The methods that we use are sometimes called a two sample t test and a two sample t confidence interval. The Independent Samples t Test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. The Independent Samples t Test is a parametric test. This test is also known as: Independent t Test; Independent Measures t Test; Independent Two-sample t Test. Two-Sample Problems Diana Mindrila, Ph.D. Phoebe Balentyne, M.Ed. Based on Chapter 19 of The Basic Practice of Statistics (6th ed.) Concepts: Two-Sample Problems. Two-Sample t Test This example will use the same data as the previous example to test whether the.
Free statistics calculators designed for data scientists.This Two Sample t test Calculator:. Compares the Mean of Two Data Samples. Assesses if Difference is Significant. Save & Recycle Data Between ProjectsUsing The Two Sample t test CalculatorFor the details about designing your test, read the guidance below.To use the calculator, enter the data from your sample as a stringof numbers, separated by commas. Adjust the calculator's settings(significance level, one or two tailed test)to match the test goals. Hit calculate.
It will compute the t-statistic,p-value, and evaluate if we should accept or reject the proposed hypothesis.For easy entry, you can copy and paste your data into theentry box from Excel. You can save your data for use withthis calculator and other calculators on this site.
Just hitthe 'save data' button. It will save the data in your browser(not on our server, it remains private).
Saved data sets willappear on the list of saved datasets below the data entry panel.To retrieve it, click the 'load data' button next to it. Interpreting Two Sample t test ResultsThis calculator is designed to evaluate statements comparingthe mean of two separate samples.We refer to this statement as the null hypothesis, a claim wewould accept in the absence of other evidence. This occurs byaccepting the alternate hypothesis, which should be a mutuallyexclusive claim. For example, in quality control, we may testthe hypothesis that two finished items came from the same batchof raw materials, by checking a property like weight or color.One of the parameters in the calculator asks you to select if youwant to run a one sided or two-sided test.
A one sided test can beused to test if the sample mean is significantly below the expectedmean for the population. The example above was a one-sample test.A two sided test looks for any significant deviation (up or down)relative to the null hypothesis. The two sided test is best whenscreening for differences, the one side test is useful if checkingfor a particular defect.Mathematically, the t-statistic is a composite of several basicmetrics from the panel.We compare the sample mean with the expected value and comparethe difference with the sample standard deviation, adjusted forsample size. The sample size is also used to calculate thedegrees of freedom for the statistical distribution.The t-statistic is converted into a probabilityvalue based on Student's t-distribution, which is used tomake the final assessment about the null hypothesis.It is critical to remember some fundamental assumptions aboutthe underlying population and sample process, particularly ifyou regularly sample. Increasing the sample size willinevitably make any result appear more significant, throughincreasing the degrees of freedom reflected in the statistic.
Applications Of Two Sample Tests
This canbe problematic if subtle factors in the underlying populationchange in the process (shift changes, time of day, operatingconditions). It often makes sense to split your experimentinto parts and seek to replicate results across differentperiods and operators to ensure your determination is accurate.