![]() In a distribution graph, a gap is an interval which contains no data on the other hand, a peak is the highest point of a data set. When having a specific shape, such as the bell shape and the u shape, is very simple to describe the shape of the distribution on the other hand, what happens when you cannot recognize any of these well known shapes? How to describe the shape of a distribution that has all kinds of curves, ups and downs? For that we need to tell if there are peaks, gaps or clusters in the distribution. How to describe the shape of a distributionįor this section, let us go back to figure 6 where the distribution is easily observed to be symmetric, still, it does not have a particular shape. In a frequency distribution graph, this means that all of the outcomes or class intervals have the exact same frequency, producing a graph with no ups, downs or any other shape but a straight horizontal line. The distribution in figure 2 could be of any type, this figure just represents how a normal distribution would look like for a more specific view of a distribution let us look at an example using a frequency distribution graph: a histogram.Ī uniform distribution shape is that which is flat (it can be perfectly flat, but not necessarily, as long as is close to being flat), this means the spread of the data is equal (or uniform) throughout the whole range of the data set. ![]() In general, a bell shaped distribution (also called a mound shaped distribution) looks like: We will talk about it more after we have a deeper understanding on what a probability distribution is in later lessons. All bell shaped distribution graphs will have its highest value in the center.īell shaped distributions are what is known in math and science as a normal (or Gaussian) distribution they are the most important probability distribution shape since it is usually the product of a sufficiently large data set from random variables found in nature. Let us learn how to determine the shape of distribution by looking at the basic figures one can find through different graphic representations of data:Ī bell shaped distribution has the shape of a mountain (a mound) or bell that is symmetrical and its axis of symmetry comes from the value of the average of its data set values. We will go into detail about the probability distribution in a later lesson, for now we will focus on the topic of shape of distribution statistics, no matter what type of distribution you are working with. ![]() How to determine the shape of a distribution On this lesson, we will be focusing on studying data distribution shapes and learning to identify the information that can be obtained just by looking to the shape of the distribution being studied. Therefore, the vertical axis in a histogram or frequency polygon is the frequency of each events outcome in the study, while the vertical axis in a probability distribution graph is the probability of the outcomes happening. In simple words, a probability distribution is yet another graphic representation of the values in a data set, the difference is that a probability distribution graph provides the probability of each specific outcome to occur, rather than its frequency. While a frequency distribution depicts the data based on the specific outcomes obtained from the study or experiment, the probability distribution will base its depiction on the chances of each possible outcome to happen. ![]() Usually a distribution is either a frequency distribution or a probability distribution, and the type of distribution depends on the basis of the arrangement (the basis taken to graph or depict the data in any way). We learned from our lesson on the frequency distribution and histograms, that a frequency distribution is a tool to organize the gathered information from a statistical study into an efficient model, where data are summarized and depicted in a manner that facilitates its communication.Ī frequency distribution orderly sorts data based on the magnitude of the observations, it accounts for the total outcomes of a survey or experiment, and presents the frequency of each outcome as it has been observed or obtained Then, the presentation of the data is done through a frequency distribution table, a histogram or even a frequency polygon.īut data is not only depicted through frequency distributions and their many graphic methods, data can also be presented through probability distributions.īefore we cover this new concept, let us remember that in general (in statistics) a distribution refers to the way data collected is presented (a graphic representation of a data set), in other words, a distribution is the way a data set has been arranged to show the spread of its values : the range the values have, how dispersed are they from each other, or close, etc.
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