AIC Calculator

Compare models using the Akaike information criterion.

About AIC Calculator

AIC balances fit against complexity, penalising each additional parameter by 2. BIC penalises parameters more heavily as sample size grows, so it favours simpler models than AIC does. Only differences in AIC are meaningful; the absolute value depends on constants that cancel when comparing models on the same data.

Formula

AIC

AIC = 2k − 2 ln(L); BIC = k ln(n) − 2 ln(L); lower is better

k: Number of parameters, penalising complexity
ΔAIC: Difference between models; under 2 is negligible

Worked example

With the default values below, AIC Calculator returns Akaike Information Criterion of 370.0000. Change any input above and every figure here is recalculated in your browser.

Inputs used

Log likelihood of the model: -180
Number of parametersaccepts 1–100: 5
Sample sizeminimum 2: 100
AIC of the competing model: 375

Results

Akaike Information Criterion: 370.0000
AIC Corrected for Small Samples: 370.6383
Bayesian Information Criterion: 383.0259
Difference From the Competing Model: -5.0000
Verdict: This model is preferred

Step by step

1Values used
2AIC
3Akaike Information Criterion= 370.0000
4AIC Corrected for Small Samples= 370.6383
5Bayesian Information Criterion= 383.0259
6Difference From the Competing Model= -5.0000
7Verdict= This model is preferred

Assumptions and limits

The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

How is AIC calculated?

AIC = 2k − 2 ln(L); BIC = k ln(n) − 2 ln(L); lower is better AIC balances fit against complexity, penalising each additional parameter by 2. BIC penalises parameters more heavily as sample size grows, so it favours simpler models than AIC does.

Why does AIC matter?

Only differences in AIC are meaningful; the absolute value depends on constants that cancel when comparing models on the same data.

What values do I need to enter?

This calculator takes 4 inputs: Log likelihood of the model, Number of parameters, Sample size, AIC of the competing model. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.