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Probability
The probability of a certain event is a number expressing the likelihood that a specific event will occur, expressed as the ratio of the number of actual occurrences to the number of possible occurrences. In mathematics, it is a measure of how often an event will happen and is the basis of statistics.
14,643 Questions
How does sample size affect the validity of an experiment?
The sample size has no effect on the validity of an experiment: instead, it is the experimental procedure and integrity of the experimenters.
The sample size can affect conclusions that may be drawn from an experiment. The larger the sample is, the more reliable these conclusions are.
What is the probability of guessing a 7 and getting it correct?
It depends on what the questionis and what you can choose from.
What does events are mutually exclusive mean?
Two events are mutually exclusive if the occurrence of one event implies that the other cannot occur. There is no need for either to occur.
For example, if you roll a die and the two outcomes of interest are:
A - you roll a prime
B - you roll a composite
then A and B cannot occur together. Of course, you could roll a 1, so that neither A nor B occurs.
An example of events that are not mutually exclusive is:
A - you roll a prime
C - you roll an even number
If you roll a 2 then both A and C occur.
What is the probability of rolling a number less than a 6 on a set of dice?
The answer depends on how many dice are in the SET of dice.
This question cannot be answered for two reasons.
- The questions states that "... the probability of each failing [is] 2 ...". That is not possible since probabilities can never be greater than 1.
- The question does not specify what even the probability is required for: the guidance system failing or not failing!
Is the number of all possible outcomes of an experiment. The number depends on the experiment.
What is the probability of it raining in spring?
The answer depends on where on earth the question is about.
The answer depends on where on earth the question is about.
The answer depends on where on earth the question is about.
The answer depends on where on earth the question is about.
Pr(4 tails | at least 3 tails) = Pr(4 tails and at least 3 tails)/Pr(at least 3 tails)
= Pr(4 tails)/Pr(at least 3 tails)
= (5/32) / (1/2) = 5/16.
Pr(4 tails | at least 3 tails) = Pr(4 tails and at least 3 tails)/Pr(at least 3 tails)
= Pr(4 tails)/Pr(at least 3 tails)
= (5/32) / (1/2) = 5/16.
Pr(4 tails | at least 3 tails) = Pr(4 tails and at least 3 tails)/Pr(at least 3 tails)
= Pr(4 tails)/Pr(at least 3 tails)
= (5/32) / (1/2) = 5/16.
Pr(4 tails | at least 3 tails) = Pr(4 tails and at least 3 tails)/Pr(at least 3 tails)
= Pr(4 tails)/Pr(at least 3 tails)
= (5/32) / (1/2) = 5/16.
What is the probability of rolling a factor of 2 on a die?
There are three factors of 2 on a regular six-sided die. Therefore, it is 3/6, or 1/2.
What is the probability of an event b that impossible?
The probability of an impossible event is 0.
The probability of an impossible event is 0.
The probability of an impossible event is 0.
The probability of an impossible event is 0.
How many number 7 in a deck of 52 cards?
There are four 7s, which are the 7 of club, 7 of heart, 7 of spade and the 7 of diamond. Each number from 1 to 10 including the Jack, Queen and King have their cards of club, heart, spade and diamond each, which makes 13 (1 to 10 plus J Q and K) x 4 (club, heart, spade and diamond) = 52 cards
Can a negative z score be greater than a positive z score?
Assume the z-score is relative to zero score. In simple terms, assume that we have 0 < z < z0, where z0 is the arbitrary value.
Then, a negative z-score can be greater than a positive z-score (yes). How? Determine the probability of P(-2 < z < 0) and P(0 < z < 1). Then, by checking the z-value table, you should get:
P(-2 < z < 0) ≈ 0.47725
P(0 < z < 1) ≈ 0.341345
What is the relationship between standard deviation and accuracy?
It depends what you're asking. The question is extremely unclear. Accuracy of what exactly? Even in the realm of statistics an entire book could be written to address such an ambiguous question (to answer a myriad of possible questions).
If you simply are asking what the relationship between the probability that something will occur given the know distribution of outcomes (such as a normal distribution), the mean of that that distribution, and the the standard deviation, then the standard deviation as a represents the spread of the curve of probability. This means that if you had a cure where 0 was the mean, and 3 was the standard deviation, the likelihood of observing a value of 12 (or -12) would be likely inaccurate if that was your prediction. However, if you had a mean of 0 and a standard deviation of 100, the likelihood of observing of a 12 (or -12) would be quite likely. This is simply because the standard deviation provides a simple representation of the horizontal spread of probability on the x-axis.
What is sampling with replacement and why is it not used?
It is used. However, it can result in the same unit being observed again. That reduced the range of possible outcomes.
It is used. However, it can result in the same unit being observed again. That reduced the range of possible outcomes.
It is used. However, it can result in the same unit being observed again. That reduced the range of possible outcomes.
It is used. However, it can result in the same unit being observed again. That reduced the range of possible outcomes.
Is binomial probability distribution always negatively skewed?
No. It depends on the probability of success, p. If p < 0.5 the distribution is positively skewed.
No. It depends on the probability of success, p. If p < 0.5 the distribution is positively skewed.
No. It depends on the probability of success, p. If p < 0.5 the distribution is positively skewed.
No. It depends on the probability of success, p. If p < 0.5 the distribution is positively skewed.
What is the probability of a spinner stopping on a composite number when number 1-8 are shown?
It is 3/8
It is 3/8
It is 3/8
It is 3/8
What is a continuous random variable?
In the simplest setting, a continuous random variable is one that can assume any value on some interval of the real numbers.
For example, a uniform random variable is often defined on the unit interval [0,1], which means that this random variable could assume any value between 0 and 1, including 0 and 1. Some possibilities would be 1/3, 0.3214, pi/4, e/5, and so on ... in other words, any of the numbers in that interval.
As another example, a normal random variable can assume any value between -infinity and +infinity (another interval). Most of these values would be extremely unlikely to occur but they would be possible. The random variable could assume values of 3, -10000, pi, 1000*pi, e*e, ... any possible value in the real numbers.
It is also possible to define continue random variables that assume values on the entire (x,y) plane, or just on the circumference of a circle, or anywhere that you can imagine that is essentially equivalent (in some sense) to pieces of a real line.
The answer depends on what the trials are, what constitutes a favourable outcome, and how many possible outcomes there were previously. And since you have not bothered to provide these crucial bits of information, I cannot provide a more useful answer.
The answer depends on what the trials are, what constitutes a favourable outcome, and how many possible outcomes there were previously. And since you have not bothered to provide these crucial bits of information, I cannot provide a more useful answer.
The answer depends on what the trials are, what constitutes a favourable outcome, and how many possible outcomes there were previously. And since you have not bothered to provide these crucial bits of information, I cannot provide a more useful answer.
The answer depends on what the trials are, what constitutes a favourable outcome, and how many possible outcomes there were previously. And since you have not bothered to provide these crucial bits of information, I cannot provide a more useful answer.
Is the uniform probability distribution is symmetric about the mode?
Yes, the uniform probability distribution is symmetric about the mode. Draw the sketch of the uniform probability distribution. If we say that the distribution is uniform, then we obtain the same constant for the continuous variable.
* * * * *
The uniform probability distribution is one in which the probability is the same throughout its domain, as stated above. By definition, then, there can be no value (or sub-domain) for which the probability is greater than elsewhere. In other words, a uniform probability distribution has no mode. The mode does not exist.
The distribution cannot, therefore, be symmetric about something that does not exist.
