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Probability is a way to measure how likely an event is to occur. When we talk about the chance of something happening, we're often discussing probability. In statistics, probability is represented as a number between 0 and 1.
For example, a probability of 0 means the event will not occur, while 1 means it is certain to happen. Probabilities can also be expressed as percentages by multiplying the value by 100. Thus, a probability of 0.44 means there is a 44% chance the event will happen.
This is important for understanding our given exercise where the chance of a three-year-old being enrolled in day care is represented as a proportion (probability) of 0.44.

Proportions help us understand parts of a whole. They are often used to describe the ratio of a specific group within a larger population.
In the given exercise, the proportion of three-year-old children in day care is 0.44. This means that out of 100 three-year-olds, 44 are expected to be enrolled in day care. Proportions are very useful in understanding distributions within a dataset.
When you see a proportion, you can also view it as a probability. For instance, if the proportion of children in day care is 0.44, the probability that a single, randomly chosen child is in day care is also 0.44, as they represent the same concept.

Random selection is a key concept in statistics and probability. It means choosing items from a dataset in such a way that every item has an equal chance of being chosen.
When we randomly select a three-year-old from the population to see if they are in day care, each child has the same likelihood of being chosen. This is crucial because it ensures that our probability calculations are fair and unbiased
. In our example, the random selection ensures that the proportion (already given as 0.44) accurately reflects the probability that any one child from the group is in day care. Random selection eliminates bias and ensures that our probabilities and proportions give us a true picture of the population.