# Binomial distribution statistics for dummies

Normal distribution describes continuous data which have a symmetric distribution, with a characteristic 'bell' shape. Binomial distribution describes the distribution of binary data from a finite sample. Thus it gives the probability of binomial distribution statistics for dummies r events out of n trials. Poisson distribution describes the distribution of binary data from an infinite sample. Thus it gives the probability of getting r events in a population. One binomial distribution statistics for dummies example is the histogram of the birth weight in kilograms of the 3, new born babies shown in Figure 1.

To distinguish the use of the same word in normal range and Binomial distribution statistics for dummies distribution we have used a lower and upper case convention throughout. The histogram of the sample data is an estimate of the population distribution of birth weights in new born babies.

We presume that if we were able to look **binomial distribution statistics for dummies** the entire population of new born babies then the distribution of birth weight would have exactly the Normal shape.

We often infer, from a sample whose histogram has the approximate Normal shape, that the population will have exactly, or as near as makes no practical difference, that Normal shape. It is symmetrically distributed around the mean. Changing the multiplier 1. For this purpose a random sample from the population is first taken. In appropriate circumstances this interval may estimate the reference interval for a particular laboratory test which is then used for diagnostic purposes.

We can use the **binomial distribution statistics for dummies** that our sample birth weight data appear Normally distributed to calculate a reference range. So a reference range for our sample of babies, using the values given in the histogram above, is:. A baby's weight at birth is strongly associated with mortality risk during the first year and, to a lesser degree, with developmental problems in childhood and the risk of various diseases in adulthood.

If the data are not Normally distributed then we can base the normal reference range on the observed percentiles of the sample, i.

In this example, the percentile-based reference range for our sample was calculated as 2. Most reference ranges are based on samples larger than people. Over many years, and millions of births, the WHO has come up with a normal birth weight range for new born babies.

Low birth weight babies are usually defined by the WHO as weighing less than g the 10th centile regardless of gestational age, and large birth weight babies are defined as weighing above kg the 90th centile. Hence the normal birth weight range is around 2. For our sample data, the 10th to 90th centile range was binomial distribution statistics for dummies, 2.

If a group of patients is given a new drug for the relief of a particular condition, then the proportion p being successively treated can be regarded as estimating the population treatment success binomial distribution statistics for dummies. Thus p also represents a mean. Data which can take only a binary 0 or 1 response, such as treatment failure or treatment success, follow the binomial distribution provided the underlying population response rate does not change.

The binomial probabilities are calculated from:. In the above, n! This area totals 0. So the probability of eight or more responses out of 20 is 0. For a fixed sample size n the shape of the binomial distribution depends only on. The number of responses actually observed can only take integer values between 0 no responses and 20 all respond.

The binomial distribution for this case is illustrated in Figure 2. The distribution is not symmetric, it has a maximum at five responses and the height of the blocks corresponds to the probability of obtaining the particular number of responses from the 20 patients yet to be treated. It binomial distribution statistics for dummies be noted that the expected value for rthe number of successes yet to be observed if we treated n patients, is nx.

The potential variation about this expectation is expressed by the corresponding standard deviation:. It is also only binomial distribution statistics for dummies situations in which reasonable agreement exists between the distributions that we would use the confidence interval expression given previously. For technical reasons, the expression given for a confidence interval for a proportion is an approximation.

The approximation will usually be quite good provided p is not too close to 0 or 1, situations in which either almost none or nearly all of the patients respond to treatment. The approximation improves with increasing sample size n. Typical examples are the number of deaths in a town from a particular disease per day, binomial distribution statistics for dummies the **binomial distribution statistics for dummies** of admissions to a particular hospital.

Wight et al looked at the variation in cadaveric heart beating organ donor rates in the UK. They found that there were organ donors, agedacross the UK for the two years and combined. Heart-beating donors are patients who are seriously ill in an intensive care unit ICU and are placed on a ventilator.

Now it is clear that the distribution of the number of donors takes integer values only, thus the distribution is similar in this respect to the binomial. However, there is no theoretical limit to the binomial distribution statistics for dummies of organ donors that could happen on a particular day. Here the population is the UK population agedover two years, which is over 82 million person years, so in this case each member can be thought to have a very small probability of actually suffering an event, in this case being admitted to a hospital ICU and placed on a ventilator with a life threatening condition.

It should be noted that the expression for the mean is similar to that forexcept here multiple data values are common; and so instead of writing each as a distinct figure in the numerator they are first grouped and counted. Here e is the exponential constant 2.

Suppose that before the study of Wight et al. Remember that 2 0 and 0! If the study is then to be conducted over 2 years dayseach of these probabilities is multiplied by to give the expected number of days during which 0, 1, 2, 3, etc. These expectations are A comparison can then be made between what is expected and what is actually observed.

The smaller the sample size, the more spread out the tails, and the larger the sample size, the closer the t- distribution is to the Normal distribution Figure 3.

The t-distribution for various sample sizes. The chi-squared distribution is continuous probability distribution whose shape is defined by the number of degrees of freedom.

It is a right-skew distribution, but as the number of degrees of freedom increases it approximates the Normal distribution Figure 4.

The chi-squared distribution is important for its use in chi-squared tests. These are often used to test deviations between observed and expected frequencies, or to determine the independence between categorical variables.

When conducting a chi-squared test, the probability values derived from chi-squared distributions can be looked up in a statistical table. The chi-squared distribution for various degrees of freedom. Skip to main content.

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Figure 1 Distribution of birth weight in 3, newborn babies data from O' Cathain et al So a reference range for our sample of babies, using the values given in the histogram above, is: The Binomial Distribution If a group of patients is given a new drug for the relief of a particular condition, then the proportion p being successively treated can be regarded as estimating the population treatment success rate.

The binomial probabilities are calculated from: The potential variation about this expectation is expressed by the corresponding standard deviation: The Normal distribution describes fairly precisely the binomial distribution in this case. In such cases the probabilities generated binomial distribution statistics for dummies the binomial distribution itself must be used.

Exact confidence intervals can be calculated as described by Altman et binomial distribution statistics for dummies. The Poisson probabilities are calculated from: Example Binomial distribution statistics for dummies that before the study of Wight et al. Other Distributions A brief description of some other distributions are given for completeness.

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Note that a die has 6 sides but here we look at only two cases: Tossing a coin three times H is for heads, T for Tails can get any of these 8 outcomes:. And this is what it looks like as a Bar Graph:. Now imagine we want the chances of 5 heads in 9 tosses: It is often called "n choose k". You can read more about it at Combinations and Permutations.

The probabilities for "two chickens" all work out to be 0. But we need to include that there are three such ways it can happen: That was a lot of work for something we knew already, but now we have a formula we can use for harder questions. Moral of the story: Have a play with the Quincunx then read Quincunx Explained to see the Binomial Distribution in action.

It is not symmetrical! What is the expected Mean and Variance of the 4 next inspections? There are relatively simple formulas for them. They are a little hard to prove, but they do work! So we can expect 3. Hide Ads About Ads. The Binomial Distribution "Bi" means "two" like a bicycle has two wheels Did we get a four We used special words: What is the probability of selling 2 chicken sandwiches to the next 3 customers? Probability of k out of n ways: For the sports bikes: