- The fixed effects model assumes that the data come from normal populations which differ in their means.
- Random effects models assume that the data describe a hierarchy of different populations whose differences are constrained by the hierarchy.
- [Mixed models]? describe situations where both fixed and random effects are present.

The fundamental technique is a partitioning of the total sum of squares into components related to the effects in the model used. For example, we show the model for a simplified ANOVA with one type of treatment at different levels. (If the treatment levels are quantitative and the effects are linear, a linear regression analysis may be appropriate.)

- SS
_{Total}= SS_{Error}+ SS_{Treatments}

The number of [degrees of freedom]? (abbreviated 'df') can be partitioned in a similar way and specifies the [Chi-squared distribution]? which describes the associated sums of squares.

- df
_{Total}= df_{Error}+ df_{Treatments}

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