Two players, Sita and Geeta, play a table tennis match. It is known that the probability of Sita winning the match is 0.59. What is the probability of Geeta winning the match?

Points to Remember:

Probability of an event: The likelihood of an event occurring.
Complementary events: Two events are complementary if one event occurs if and only if the other does not. The sum of probabilities of complementary events is always 1.

Introduction:

This question involves calculating the probability of a complementary event in a simple probability scenario. We are given the probability of Sita winning a table tennis match against Geeta (P(Sita wins) = 0.59). Probability, in its simplest form, is the ratio of favorable outcomes to the total number of possible outcomes. In this case, the total number of possible outcomes is two: either Sita wins or Geeta wins. These are mutually exclusive and exhaustive events.

Body:

1. Understanding Complementary Events:

In this table tennis match, there are only two possible outcomes: either Sita wins or Geeta wins. These are complementary events. If Sita does not win, then Geeta must win, and vice versa. This means that the sum of the probabilities of Sita winning and Geeta winning must equal 1 (or 100%).

2. Calculating the Probability of Geeta Winning:

We can represent the probability of Geeta winning as P(Geeta wins). Since the events are complementary, we can use the following formula:

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P(Geeta wins) = 1 – P(Sita wins)

Substituting the given value:

P(Geeta wins) = 1 – 0.59 = 0.41

Therefore, the probability of Geeta winning the match is 0.41 or 41%.

Conclusion:

In summary, the probability of Geeta winning the table tennis match is 0.41, derived from the fact that the probabilities of Sita winning and Geeta winning are complementary events and must sum to 1. This simple calculation demonstrates a fundamental concept in probability theory. This understanding of complementary probabilities is crucial in various fields, from sports analytics to risk assessment and decision-making in many areas of life. A deeper understanding of probability helps in making informed decisions based on the likelihood of different outcomes. Further exploration of conditional probability and other probability distributions would provide a more comprehensive understanding of probabilistic reasoning.

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