Which situation does NOT describe a uniform probability model?
Option D, selecting a ball from 3 yellow balls and 2 red balls, does not describe a uniform probability model because the probability of selecting a yellow ball is not the same as the probability of selecting a red ball.
A situation that does not describe a uniform probability model is when the probability of each outcome is not equal.
A selecting a day of the week using only the first letter of that dayselecting a day of the week using only the first letter of that day
B an even or odd sum from rolling two dicean even or odd sum from rolling two dice
C the number of heads and tails from flipping two coinsthe number of heads and tails from flipping two coins
D selecting a ball from 3 yellow balls and 2 red balls
(for the bot)
A uniform probability model is a model where each outcome has an equal probability of occurring. Therefore, any situation that does not have all outcomes with equal probabilities would not describe a uniform probability model. Here are a few examples:
1. A biased coin flip: If a coin is biased and more likely to land on heads than tails, then it does not follow a uniform probability model because the probabilities of getting heads and tails are unequal.
2. Rolling a loaded die: If a die is loaded and more likely to land on certain numbers than others, then it does not follow a uniform probability model because the probabilities of rolling different numbers are unequal.
3. Drawing cards from a deck with missing or extra cards: If you have a deck of cards but some cards are missing or duplicated, the probabilities of drawing each card will not be equal, so it does not follow a uniform probability model.
In general, any situation where the probabilities of different outcomes are not equal does not describe a uniform probability model.