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What happens when t test assumptions are violated?
If the assumption of normality is violated, or outliers are present, then the paired t test may not be the most powerful test available, and this could mean the difference between detecting a true difference or not. A nonparametric test or employing a transformation may result in a more powerful test.
What are the consequences if the assumption of normality is violated?
There are few consequences associated with a violation of the normality assumption, as it does not contribute to bias or inefficiency in regression models. It is only important for the calculation of p values for significance testing, but this is only a consideration when the sample size is very small.
What are the three assumptions of the t test?
The common assumptions made when doing a t-test include those regarding the scale of measurement, random sampling, normality of data distribution, adequacy of sample size, and equality of variance in standard deviation.
What are assumptions made when conducting a t-test?
T-Test Assumptions. The first assumption made regarding t-tests concerns the scale of measurement. The assumption for a t-test is that the scale of measurement applied to the data collected follows a continuous or ordinal scale, such as the scores for an IQ test. The second assumption made is that of a simple random sample,…
When does your data violate one-sample t test assumptions?
If you find outliers in your data that are not due to correctable errors, you may wish to consult a statistician as to how to proceed. If the population from which the data were sampled is skewed, then the one-sample t test may incorrectly reject the null hypothesis that the population mean is the hypothesized value even when it is true.
How to check the results of a t-test?
The easiest way to check this assumption is to verify that each observation only appears in each sample once and that the observations in each sample were collected using random sampling. If this assumption is violated, the results of the two sample t-test are completely invalid.
When to use a two sample t test?
A two sample t-test is used to test whether or not the means of two populations are equal. 1. Independence: The observations in one sample are independent of the observations in the other sample. 2. Normality: Both samples are approximately normally distributed. 3. Homogeneity of Variances: Both samples have approximately the same variance.