T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases, and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to mis-diagnosis if diagnostically-relevant TCRs remain undetected. To address this issue, we developed TCRPower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth, and read cut-off. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones as spike-in TCRs. We sequenced the spike-in TCRs from T-cell clones, together with TCRs from peripheral blood, using a 5’ RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cut-off, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0%, TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease relevant TCRs for diagnostic applications.