Best Answer

Yes, it is possible for two dependent events to have the same probability of occurring. The probability of an event is dependent on the outcomes of other events, and it is influenced by the relationship between these events. So, it is conceivable for two dependent events to have equal probabilities.

More answers

Yes.

Q: Is it true that two dependent events can have the same probability of occurring?

Write your answer...

Submit

Still have questions?

Continue Learning about Math & Arithmetic

No, the combined probability is the product of the probability of their separate occurrances.

They are "events that have the same probability". Nothing more, nothing less.

The probability of winning two games with the same probability of 0.8 can be calculated by multiplying the probability of winning the first game (0.8) by the probability of winning the second game (0.8). Therefore, the probability is 0.8 * 0.8 = 0.64, or 64%.

No, two events are independent if the outcome of one does not affect the outcome of the other. They may or may not have the same probability. Flipping two coins, or rolling two dice, are independent. Drawing two cards, however, are dependent, because the removal of the first card affects the possible outcomes (probability) of the second card.

equiprobable events.

Related questions

No, the combined probability is the product of the probability of their separate occurrances.

They are "events that have the same probability". Nothing more, nothing less.

If the probability of an event occurring is p, then 1-p represents the probability of the same event not occurring. The value of p must lie between 0 and 1.

The probability of winning two games with the same probability of 0.8 can be calculated by multiplying the probability of winning the first game (0.8) by the probability of winning the second game (0.8). Therefore, the probability is 0.8 * 0.8 = 0.64, or 64%.

No, two events are independent if the outcome of one does not affect the outcome of the other. They may or may not have the same probability. Flipping two coins, or rolling two dice, are independent. Drawing two cards, however, are dependent, because the removal of the first card affects the possible outcomes (probability) of the second card.

Basic Rules of Probability:1) The probability of an event (E) is a number (fraction or decimal) between and including 0 and 1. (0â‰¤P(E)â‰¤1)2) If an event (E) cannot occur its probability is 0.3) If an event (E) is certain to occur, then the probability if E is 1. This means that there is a 100% chance that something will occur.4) The sum of probabilities of all the outcomes in the sample space is 1.Addition Rules/Formulas:When two events (A and B) are mutually exclusive, meaning that they can't occur at the same time or they have no outcomes in common, the probability that A or B will occur is:P(A or B)= P(A)+P(B)If A and B are not mutually exclusive, then:P(A or B)= P(A)+P(B)-P(A and B)Multiplication Rules/Formulas:When two events (A and B) are independent events, meaning the fact that A occurs does not affect the probability of B occurring (for example flipping a coin, rolling a die, or picking a card), the probability of both occurring is:P(A and B)= P(A)P(B)Conditional Probability-When two events are dependent (not independent), the probability of both occurring is:P(A or B)= P(A)P(B|A)Note: P(B|A) does not mean B divided by A but the probability of B after A.

equiprobable events.

They are both measures of the probability of an event occurring.

Equally likely events.

Equal

No. There are 24 hours in a day, not 12!

Poisson distribution shows the probability of a given number of events occurring in a fixed interval of time. Example; if average of 5 cars are passing through in 1 minute. probability of 4 cars passing can be calculated by using Poisson distribution. Exponential distribution shows the probability of waiting times between occurrences of events. If we use the same example; probability of a car coming in next 40 seconds can be calculated by using exponential distribution. -Poisson : probability of x times occurrence -Exponential : probability of waiting times between events.