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Two of the main problems encountered in the development and accuratevalidation of photometric redshift (photo-z) techniques are the lack ofspectroscopic coverage in feature space (e.g. colours and magnitudes) and themismatch between photometric error distributions associated with thespectroscopic and photometric samples. Although these issues are well known,there is currently no standard benchmark allowing a quantitative analysis oftheir impact on the final photo-z estimation. In this work, we present twogalaxy catalogues, Teddy and Happy, built to enable a more demanding andrealistic test of photo-z methods. Using photometry from the Sloan Digital SkySurvey and spectroscopy from a collection of sources, we constructed datasetswhich mimic the biases between the underlying probability distribution of thereal spectroscopic and photometric sample. We demonstrate the potential ofthese catalogues by submitting them to the scrutiny of different photo-zmethods, including machine learning (ML) and template fitting approaches.Beyond the expected bad results from most ML algorithms for cases with missingcoverage in feature space, we were able to recognize the superiority of globalmodels in the same situation and the general failure across all types ofmethods when incomplete coverage is convoluted with the presence of photometricerrors - a data situation which photo-z methods were not trained to deal withup to now and which must be addressed by future large scale surveys. Ourcatalogues represent the first controlled environment allowing astraightforward implementation of such tests. The data are publicly availablewithin the COINtoolbox (https://github.com/COINtoolbox/photoz_catalogues).

On the realistic validation of photometric redshifts, or why Teddy will never be Happy