Sample size is a critical factor in obtaining a reliable result from significance testing. Generally, a larger sample improves the odds of determining a statistically significant result. A smaller sample has a higher risk of producing false-positive or false-negative results. Samples, however, can be too large. Tracking a larger sample takes more time and money, and the reliability may not improve.