True.

In the context of a likelihood function, the argument refers to the potential or hypothesized value of the parameter, while the observed data is considered fixed. The likelihood function represents how likely the observed data is, given a specific value of the parameter. This contrasts with a probability distribution, where the parameter is fixed, and the data is the variable.

Would you like further details or have any questions?

Here are 5 related questions:

1. How is the likelihood function different from a probability density function (PDF)?
2. What is the relationship between maximum likelihood estimation (MLE) and the likelihood function?
3. Can you explain the concept of log-likelihood in statistical modeling?
4. How does the likelihood function apply in Bayesian statistics?
5. What are the common applications of the likelihood function in real-world data analysis?

Tip: In maximum likelihood estimation (MLE), the goal is to find the parameter value that maximizes the likelihood function, i.e., the parameter that makes the observed data most probable.