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Explanation:
The given statement is claiming something like [tex]P(X < 2)[/tex] produces a different result compared to [tex]P(X \le 2)[/tex]. The subtle difference of course is the "or equal to".
When talking about continuous random variables, the two values are the same. Why? Because probability of selecting *exactly* X = 2 is going to be zero. Think of a very very thin strip of height P(X = 2) and the width is approaching zero. The width is doing this because we're trying to pinpoint X = 2 itself. As you can see, this skinny rectangle will have its area approach 0. Ultimately, whether we compute [tex]P(X < 2)[/tex] or [tex]P(X \le 2)[/tex], it doesn't matter. They both produce the same result.
If you are a visual learner, consider the standard normal distribution. Now consider the task if shading to the left of z = 2. This area describes both probabiltiies mentioned above.
If X is a continuous random variable, then [tex]P(X < 2) = P(X \le 2)[/tex] and we can change the "2" to any number we want to have the same outcome.
Therefore, the original claim is false.
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Extra info:
Now if X was discrete (say from the binomial distribution), then the two probabilities would be different. This is because
[tex]P(X < 2) = P(0) + P(1)\\\\P(X \le 2) = P(0) + P(1) + P(2)\\\\P(X < 2) \ne P(X \le 2) \ \text{only if X is discrete}[/tex]
We can see in this case that the second line has P(2) while the first one does not.
So the original claim would be true if we changed "continuous" to "discrete".