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The three-component finite Gaussian mixture with a common variance has the largest displayed maximized log-likelihood, but it also has more fitted parameters. For $k$ components there are $k-1$ free weights, $k$ means and one common variance, making $d=2k$ residual-distribution parameters. Applying the nominal Akaike information criterion and Bayesian information criterion to the three residual fits givesBoth criteria favour the two-component model. It improves the likelihood substantially over one normal component; the third component gains only $1.5550$ in log-likelihood at a cost of two further parameters. The Akaike information criterion difference between two and three components is small, about $0.89$, so that criterion alone gives only a slight preference; the Bayesian information criterion preference for two is stronger. Their nearly indistinguishable overlaid curves also support retaining the simpler two-component fit.

These calculations are model-selection aids, not an exact test. Ordinary chi-squared calibration of a likelihood-ratio test for the number of mixture components is invalid in general: under a smaller-component null, some weights lie on the boundary and extra-component parameters are unidentified. This is nonregular mixture model selection. A parametric bootstrap or predictive cross-validation is preferable for a formal comparison, and should account for the mean-adjustment stage. The table uses the provided residual likelihoods and their nominal parameter counts; first-stage regression uncertainty and possible local EM maxima remain qualifications. On the supplied evidence, select two components without claiming that two distinct biological groups have been established.