the earlier number derived from the WMAP data. In the table below, figures are within 68% confidence limits for the base ΛCDM model. {| class="wikitable" style="text-align:center;" |+ Cosmological parameters from 2015 Planck results |- ! Parameter !! Symbol !! TT + lowP !! TT + lowP + lensing !! TT + lowP + lensing + ext !! TT, TE, EE + lowP !! TT, TE, EE + lowP + lensing !! TT, TE, EE + lowP + lensing + ext |- ! Age of the universe (Ga) | || || || || || || |- ! Hubble constant () | || || || || || || |} Legend: TT, TE, EE: Planck Cosmic microwave background (CMB) power spectra lowP: Planck polarization data in the low-ℓ likelihood lensing: CMB lensing reconstruction ext: External data (BAO+JLA+H0). BAO: Baryon acoustic oscillations, JLA: Joint Light-curve Analysis, H0: Hubble constant In 2018, the Planck Collaboration updated its estimate for the age of the universe to billion years. Assumption of strong priors Calculating the age of the universe is accurate only if the assumptions built into the models being used to estimate it are also accurate. This is referred to as strong priors and essentially involves stripping the potential errors in other parts of the model to render the accuracy of actual observational data directly into the concluded result. This is not a valid procedure in all contexts, as noted in the accompanying caveat: "on the assumption that the project's underlying model is correct". However, the age given is thus accurate to the specified error, since this error represents the error in the instrument used to gather the raw data input into the model. The age of the universe based on the best fit to Planck 2018 data alone is billion years. This number represents an accurate "direct" measurement of the age of the universe, in contrast to other methods which typically involve Hubble's law and the age of the oldest stars in globular clusters, etc. It is possible to use different methods for determining the same parameter (in this case, the age of the universe) and arrive at different answers with no overlap in the "errors". To best avoid the problem, it is common to show two sets of uncertainties; one related to the actual measurement and the other related to the systematic errors of the model being used. An important component to the analysis