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9709 · 6.3

Continuous random variables — common mistakes

Common exam mistakes on 9709 Continuous random variables. Learn what loses marks, then practise the topic with Examiner’s Ink.

Exam tip 1

Always use the full defined range of xx for the limits of integration when calculating E(X)E(X) and E(X2)E(X^2). A common error is to use the limits from a probability calculation (e.g., P(X>1)P(X>1)) instead of the full range (e.g., 0 to 2).

Why is the probability of a continuous random variable taking a single value, P(X=c), equal to zero?

Because probability is represented by the area under the PDF curve. The area of a region with zero width (at a single point 'c') is zero. Think of it as integrating from c to c: ccf(x)dx=0\int_{c}^{c} f(x) dx = 0.

What is the difference between a PDF, f(x), and a CDF, F(x)?

The PDF, f(x)f(x), is the 'density' function; its value is not a probability, but the area under it is. The CDF, F(x)F(x), is a cumulative probability function; its value at xx is the actual probability P(Xx)P(X \le x). The PDF is the derivative of the CDF.

My variance calculation gave a negative number. What did I do wrong?

Variance can never be negative. If you get a negative answer, you have made a calculation error. The most common mistakes are: (1) an error in the integration of x2f(x)x^2 f(x), or (2) forgetting to square E(X)E(X) in the formula Var(X)=E(X2)[E(X)]2Var(X) = E(X^2) - [E(X)]^2. The worked example above shows how to spot and correct this.

How do I define the CDF for a piecewise PDF?

You need to define F(x)F(x) for all real numbers. It will be 0 for xx less than the lower bound of the PDF's range. It will be 1 for xx greater than the upper bound. In between, you integrate the PDF from the lower bound up to a variable xx. If the PDF has multiple pieces, the CDF will also have multiple corresponding pieces.