Here are the essential concepts you must grasp in order to answer the question correctly.
Independence of Observations
One-way ANOVA requires that the observations within each group are independent of one another. This means that the data collected from one group should not influence or be related to the data from another group. Independence is crucial to ensure that the results of the ANOVA test are valid and that any differences observed are due to the treatment rather than confounding factors.
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Normality
The assumption of normality states that the data in each group should be approximately normally distributed. This is important because ANOVA relies on the F-distribution, which assumes that the underlying populations are normally distributed. If the sample sizes are large enough, the Central Limit Theorem may mitigate this requirement, but for smaller samples, normality should be checked.
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Homogeneity of Variances
Homogeneity of variances, or homoscedasticity, means that the variances among the different groups being compared should be roughly equal. This assumption is critical for the validity of the ANOVA results, as unequal variances can lead to inaccurate conclusions. Tests such as Levene's test can be used to assess this condition before performing a one-way ANOVA.
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