We consider the problem of detecting a deformation from a symmetric Gaussian random ptensor (p ≥ 3) with a rank-one spike sampled from the Rademacher prior. Recently in Lesieur et al. [30], it was proved that there exists a critical threshold β p so that when the signal-to-noise ratio exceeds β p , one can distinguish the spiked and unspiked tensors and weakly recover the prior via the minimal mean-square-error method. On the other side, Perry, Wein, and Bandeira [44] proved that there exists a β p < β p such that any statistical hypothesis test can not distinguish these two tensors, in the sense that their total variation distance asymptotically vanishes, when the signa-to-noise ratio is less than β p . In this work, we show that β p is indeed the critical threshold that strictly separates the distinguishability and indistinguishability between the two tensors under the total variation distance. Our approach is based on a subtle analysis of the high temperature behavior of the pure p-spin model with Ising spin, arising initially from the field of spin glasses. In particular, we identify the signal-to-noise criticality β p as the critical temperature, distinguishing the high and low temperature behavior, of the Ising pure p-spin mean-field spin glass model.