The article explores different ways to create likelihood functions for a parameter-dependent function in statistics. By allowing certain statistics to vary with the parameter, new methods for constructing likelihood functions are investigated. The study shows that these parameter-dependent functions can be used to estimate likelihood functions more accurately, especially when using maximum likelihood estimators. The findings provide insights into how to better analyze data and make statistical inferences in various scientific fields.