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Dependent Samples T-test Standard Error

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counter Wizard Graphing Scientific Financial Calculator books AP calculator review Statistics AP study guides Probability Survey paired sample t-test example sampling Excel Graphing calculators Book reviews Glossary AP practice exam Problems and solutions Formulas Notation Share with Friends Hypothesis Test: Difference Between Paired Means This matched pairs t test example lesson explains how to conduct a hypothesis test for the difference between paired means. The test procedure, called the matched-pairs t-test, is appropriate when the following conditions are met: The sampling method for each sample is simple random sampling. The test is conducted on paired data. (As a result, the data sets

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are not independent.) The sampling distribution is approximately normal, which is generally true if any of the following conditions apply. The population distribution is normal. The population data are symmetric, unimodal, without outliers, and the sample size is 15 or less. The population data are slightly skewed, unimodal, without outliers, and the sample size is 16 to 40. The sample size is greater than 40, without outliers. This approach consists of four steps: (1) state the hypotheses, (2) formulate an analysis plan, (3) analyze sample data, and (4) interpret results. State the Hypotheses Every hypothesis test requires the analyst to state a null hypothesis and an alternative hypothesis. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false; and vice versa. The hypotheses concern a new variable d, which is based on the difference between paired values from two da

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Models[ View All ]Factor Analysis & SEMConduct and Interpret a Factor AnalysisExploratory Factor AnalysisConfirmatory Factor Analysis[ View All ]Non-Parametric AnalysisCHAIDWald Wolfowitz Run Test[ View All ] CloseDirectory Of Survey http://stattrek.com/hypothesis-test/paired-means.aspx?Tutorial=AP InstrumentsAttitudesEmotional IntelligenceLearning / Teaching / SchoolPsychological / PersonalityWomenCareerHealthMilitarySelf EsteemChildLeadershipOrganizational / Social GroupsStress / Anxiety / Depression Close CloseFree ResourcesNext Steps Home | Academic Solutions | Directory of Statistical Analyses | (M)ANOVA Analysis | Paired Sample T-Test Paired Sample T-Test Paired sample t-test is a statistical technique that is used to compare two population means in the case of http://www.statisticssolutions.com/manova-analysis-paired-sample-t-test/ two samples that are correlated.  Paired sample t-test is used in ‘before-after’ studies, or when the samples are the matched pairs, or when it is a case-control study.  For example, if we give training to a company employee and we want to know whether or not the training had any impact on the efficiency of the employee, we could use the paired sample test.  We collect data from the employee on a seven scale rating, before the training and after the training.  By using the paired sample t-test, we can statistically conclude whether or not training has improved the efficiency of the employee.  In medicine, by using the paired sample t-test, we can figure out whether or not a particular medicine will cure the illness. Click to Start Using Intellectus Statistics for Free Steps: 1. Set up hypothesis: We set up two hypotheses.  The first is the null hypothesis, which assumes that the mean of two paired samples are equal. The second hypothesis will be an alternative hypothesis, which assumes that the

related groups on the same continuous, dependent variable. For example, you could use a dependent t-test to understand whether there was a difference in smokers' daily cigarette consumption before and after a 6 week hypnotherapy programme (i.e., your dependent variable would be https://statistics.laerd.com/spss-tutorials/dependent-t-test-using-spss-statistics.php "daily cigarette consumption", and your two related groups would be the cigarette consumption values "before" and "after" the hypnotherapy programme). If your dependent variable is dichotomous, you should instead use McNemar's test. This "quick start" guide shows you how to carry out a dependent t-test using SPSS Statistics, as well as interpret and report the results from this test. However, before we introduce you to this procedure, you need to understand the different assumptions that t test your data must meet in order for a dependent t-test to give you a valid result. We discuss these assumptions next. SPSS Statistics Assumptions When you choose to analyse your data using a dependent t-test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a dependent t-test. You need to do this because it is only appropriate to use a dependent t-test if paired sample t-test your data "passes" four assumptions that are required for a dependent t-test to give you a valid result. In practice, checking for these four assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task. Before we introduce you to these four assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated (i.e., is not met). This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out a dependent t-test when everything goes well! However, don't worry. Even when your data fails certain assumptions, there is often a solution to overcome this. First, let's take a look at these four assumptions: Assumption #1: Your dependent variable should be measured on a continuous scale (i.e., it is measured at the interval or ratio level). Examples of variables that meet this criterion include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg), and so forth. You can learn more about continuous variables in our article: Types of Variab

 

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