The correct answer is False.
The cumulative distribution function (CDF) of a random variable $X$ is the probability that $X$ will be less than or equal to a certain value $x$. It is denoted by $F_X(x)$. The probability density function (PDF) of a random variable $X$ is the derivative of the CDF. It is denoted by $f_X(x)$.
The CDF is a non-decreasing function, while the PDF is a non-negative function. The CDF can be used to calculate the probability that $X$ will be in a certain range of values, while the PDF can be used to calculate the probability that $X$ will take on a certain value.
The CDF and PDF are related by the following equation:
$$F_X(x) = \int_0^x f_X(t) dt$$
This equation shows that the CDF is the integral of the PDF. The integral of a function is the area under the curve of the function. Therefore, the CDF is the area under the curve of the PDF.
The CDF and PDF are both important tools for understanding the probability distribution of a random variable. The CDF can be used to calculate the probability that $X$ will be in a certain range of values, while the PDF can be used to calculate the probability that $X$ will take on a certain value.