Missions to Mars progressively reveal the past and present habitability of the red planet. The current priority for Mars science is the recognition of definitive biosignatures of past or present life. Success of life detection missions requires choices of the best mission design, location on Mars and particular sample to be analysed. It is essential therefore to incorporate as much information as possible into the mission planning stages to maximise the precious opportunities provided by robotic operation on Mars. Bayesian statistics allow us to accommodate the many unknowns associated with a mission that has yet to take place. We have used Bayesian statistics to reveal that although in situ missions are less complex the overall probabilities of a successful mission to detect biosignatures on Mars are higher for sample return. If a mission has been designed with safe landing and operation as a priority, recognising and avoiding those samples that do not contain the target biosignature is the most important characteristic, while for a mission where the best possible samples have been targeted the probability that the sample contains the target biosignature and that it can be correctly detected is the most dominant issue. Usefully, Bayesian statistics can be used to evaluate the chances of detecting past or present life for missions to different landing sites on Mars. A comparative assessment of Eberswelde Crater and Gale Crater indicates a higher probability of success for the latter and the probabilities of success are consistently higher for the sample return mission variant. Bayesian statistics, therefore, can inform future Mars mission planning steps to help maximise the possibility of success.