Bandit algorithms sequentially accumulate data using adaptive sampling policies, offering flexibility for real-world applications. However, excessive sampling can be costly, motivating the devolopment of early stopping methods and reliable post-experiment conditional inferences. This paper studies early stopping methods in linear contextual bandits, including both pre-determined and online stopping rules, to minimize in-experiment regrets while accounting for sampling costs. We propose stopping rules based on the Opportunity Cost and Threshold Method, utilizing the variances of unbiased or consistent online estimators to quantify the upper regret bounds of learned optimal policy. The study focuses on batched settings for stability, selecting a weighed combination of batched estimators as the online estimator and deriving its asymptotic distribution. Online statistical inferences are performed based on the selected estimator, conditional on the realized stopping time. Our proposed method provides a systematic approach to minimize in-experiment regret and conduct robust post-experiment inferences, facilitating decision-making in future applications.