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There are probably many probability distributions that have just one parameter. The most important one for statistical analysis is probably the Student t distribution.
This probability distribution is fully described by a single parameter which is often called "degrees of freedom". The parameter describes the scale of the distribution, and not the location, since the Student t distribution is always centered at zero (unlike the normal distribution, which has a scale parameter, the variance, and a location parameter, the mean).
Another example of a distribution that is described with a single parameter is the exponential distribution. Unlike the Student t distribution, it is a distribution that takes only positive values.
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What is a confidence interval?
Answers.com says it is: A statistical range with a specified probability that a given parameter lies within the range. I think that means, just how confident you are that your statistical analysis is correct.
What is the relationship between the Z-score for Normal Distribution and Continuous Probability?
There is no real relationship. Probabilities for the Normal distribution are extremely difficult to work out. The z-score is a method used to convert any Normal distribution into the Standard Normal distribution so that its probabilities can be looked up in tables easily. There are infinitely many types of continuous probability distributions and the Normal is just one of them.
Differences between probability samplingand non-probability sampling?
the difference is just that non-probability sampling does not involve random selection, but probability sampling does.
How can the Poisson distribution be applied to a continuous frequency distribution?
Let L(t) be the instantaneous average rate of occurrences per unit time, at time t. So, for the ordinary Poisson distribution with parameter L, we just have L(t)=L for all t.Let I be the integral of L(t) dt over a certain time interval [0,T], say.Then, assuming that L(t) is continuous, or maybe just Riemann integrable, the total number of occurrences during [0,T] simply follows a Poisson distribution with parameter I. This is the simple answer one might expect.To prove this (SKETCH: further estimates are needed to make this really rigorous): divide [0,T] into many small intervals [tj, tj+1). In each interval, the number of occurrences is approximately Poisson with parameter L(tj)(tj+1-tj).The occurrences in each small interval are all independent of each other; hence the total number in [0,T], which is the sum of all these, follows a Poisson distribution with parameter the sum of L(tj)(tj+1-tj).As you make the maximum size of the intervals shrink to zero, this sum tends towards I, the Riemann integral of L(t)dt over [0,T], as required.
What is the probability that they you will get two heads when you toss two coins?
If you toss them enough times, the probability is 1. For just one toss the probability is 1/4.
