Abstract: Accurate evaluation of corona resistance life time of enamelled wires has been a challenge in the industry due to the considerable dispersion of test data, which makes it difficult to determine whether significant differences exist among laboratories. Clarifying the distribution pattern within a single group of data is a prerequisite for conducting significance analysis between groups. Based on this, the corona resistance life data of enamelled wires were analyzed using several typical life distribution functions, including the normal distribution, lognormal distribution, and two-parameter and three-parameter Weibull distributions. Linear regression analysis was performed, and the goodness of fit was evaluated by comparing the correlation coefficients. Results showed that the three-parameter Weibull distribution yielded the highest correlation coefficient, followed by the lognormal distribution, while the normal distribution exhibited the lowest value. This indicated that the three-parameter Weibull distribution could better characterize the distribution characteristics of the corona resistance life data of enamelled wires, whereas the normal distribution was not suitable for such analysis. Further analysis revealed that the lognormal distribution model underestimated the corona resistance life at low failure probabilities and overestimated it at high failure probabilities, suggesting that it was not appropriate for studying corona resistance life under extreme failure probabilities. In contrast, the three-parameter Weibull distribution model demonstrated higher fitting accuracy across the entire life range, providing more reliable predictions.