# Different Ways of Measuring Information in a Data Set

There are many concepts 1 have learned so far in the course that has helped me un- derstand statistics better. Using concepts such as sample, population, mean, median, mode, standard deviation, coefficient of variation, 2 score, and probability. To start off with Sample size, Data sampling is defined as a statistical analysis technique primarily used to select, manipulate and also analyze a smaller portion of data points in order to identify similar or different patterns and trends in a larger data set. Sam- ple is great when you are trying to solve things like population, experiments, even maybe trying to figure out how often most people work out at the gym.

It is a very convenient way to gather information especially since one does not have to survey an entire group. This saves time and allows faster data analysis. Going off of population, it is another great measure of tendency when taking on data. For example you can survey the popula- tion of a college campus, in order to find out how many of them drink under age.

Thus it will give you an idea of the number of students across America at other institutions. Other sources of information to collect data include the big three, mean, median, and mode. To find the mean, add up the values in the data set and then divide by the num- ber of values that you added, Essentially the mean is the average for the set of data for all the information given. Median selects the middle as the average, so that way you can get a good average out of a giant segment of data that vary only a little bit, For example how to pick an average test score for the class besides using mean, you could easily use me- dian, Another time when we usually prefer the median over the mean (or mode) is when our data is skewed.

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The median is also convenient because you don’t really need to do any kind of math to solve, if you cannot access math related tools This also applies to the next tool to measure tendency, mode. Mode is essentially the most common, so that means whatever information, num- ber or sets of data come up the most you use that to determine that norm for the informa- tion It is very easy to organize and figure out especially if you have a lot of data and don’t want to have to sift through and find a median. For example if you are looking to get a raise and you want to see what most people are getting paid at the company, you would use mode to figure out a good offer for an increased salary, plus this is a easy way to use mode for real life situations. A different way to collect and analyze data is using the standard deviation Stan- dard deviation is a number that is used to describe how measurements for a group or col- lected amount of information are spread out from the average, also known as mean, or the expected value.

A low standard deviation translates that most of the numbers are very close to the average, which is important for very small differences within a data set, loike body temperature for example or measuring chemicals in a scientific solution. The next way to measure and gather information and organize it is using the coefficient of varia- tion which is a measure of relative variability for a given data set. It is the ratio of the standard deviation of the average (mean) Z—score is another way to analyze data, it is a numerical measurement of different values relationship to the mean in a group of scat- tered values, Another way to measure frequency of data is to use probability. Probability

is convenient because you can predict information for a given data set and predict future values, probability is very important for things like stock trade and determining invest- ments in things like start up companies, or even gambling. If you can use probability to solve the frequency for different information it can be valuable, however you never want to rely just on probability, but it is a great way to forecast future information. These are the different way we can measure information in a data set, all of these are very useful in programs such as excel, which is beneficial to corporations.