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False, If other factors are held constant, the larger the values for the two sample variances, the greater the likelihood that the independent-measures t test will find a significant difference.
What is meant by hypothesis testing?
- Hypothesis testing is a form of statistical inference that uses data from a sample to draw conclusions about a population parameter or a population probability distribution.
- First, a tentative assumption is made about the parameter or distribution. This assumption is called the null hypothesis and is denoted by H0.
- If other factors are held constant, the larger the values for the two sample variances, the greater the likelihood that the independent-measures t test will find a significant difference.
- A two-factor ANOVA consists of three separate hypothesis tests.
- More the variance, more the standard error for the test. The test statistic is inversely proportional to the standard error.
- Therefore more the standard error, lower the value of the test statistic and therefore lesser likelihood that the test would be significant because more significance for a greater test statistic value.
Learn more about hypothesis testing
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