A statistic is a numerical number that represents or communicates an information on a given subject. On the other hand, the branch of study called Statistics entails the use of scientific skills to collect, organize, analyze, and make inferences from a given data to aid in making more effective decisions. Most of the courses taken as college level often incorporate the subject on Statistics regardless of the degree or diploma being acquired. The study of statistics is very important to all professional as it is applicable in all fields of professional practice in one way or another. The knowledge and skills taught in this subject are crucial in performance as they teach professionals on how to make well-informed professional and personal decisions. This is because the use of statistical knowledge in decision making relies on scientifically analyzed data which gives the most accurate results for any action taken. A conclusion derived through a statistic is usually made from examining different hypothesis and establishing their credibility. It is therefore very important that all professionals use the knowledge of statistics while making any informed business decisions to achieve the best results in any venture (Lind et. Al., 2015).
Generally, Statistics is categorized into two major branches which are descriptive and inferential forms. The two types of statistics as named above have different applications although they can be used together. To begin with, descriptive statistics is the type that deals with organizing, summarizing, and presenting data in an informative and meaningful way which allows simpler interpretation of the data provided. For instance, given a set of data that describes several animals that are sold in a pet shop that includes dogs, cats, and birds. If 100 pets are sold in a sale and 30 of them were dogs, then a descriptive statistic could be derived that 30% of the animals sold were dogs. The number of dogs, cats, and birds sold can be represented in different forms such as graphs, pie charts, and histograms to show a good representation of the sales made by the shop during a period (Lind et. Al., 2015).
The second category of statistics, inferential type, is on the other hand used for making conclusions from a given population data. As such, inferential statistics gives the procedure of drawing conclusions about a given data which are based on random variations. It means that inferential statistics involves the methods used to estimate a property of a population based on the sample. An example of the inferential statistics is whereby television networks monitor the popularity of different programs by surveying the viewership preferences from their customers (Lind et. Al., 2015). If a program has a high viewership rating, then the networks can use this information to infer about the advertising rates to apply regarding the same program. As such, this is a decision which will affect their revenue in the long run.
In statistics, the terms population and sample are often used amply while describing different sets of data. According to statistical terms, a population is a complete set of individuals or objects of interest or the measurements obtained from all individuals or objects of interest (Lind et. Al., 2015). On the other hand, a sample represents a portion, part, of the population of interest. Samples are useful in giving reliable approximations of population parameters. This process of using samples to estimate population properties is called inferential statistics.
There are two categories of variables classified under statistics, which are qualitative and quantitative. The first qualitative types of variables are used to describe all the non-numeric characteristics of a given sample set. Some examples of qualitative variables include gender, beverage preference, type of vehicle owned, and eye color among other characteristics. They are therefore useful in giving descriptive features of the sample and can be summarized in the form of charts and bars. Quantitative variables, on the other hand, are used to describe all numeric samples and are either discrete or continuous in nature. Examples of discrete quantitative variables include several cars counted, the number of rooms in a house, the number of students in a class, and the number of fruits in a basket among others. Continuous quantitative variables, on the other hand, include the air pressure in a tire, the weight of mangoes in a sack, and the duration of traveling within a distance (Lind et. Al., 2015). As such, continuous variables are not counted like discrete ones but they are measured using the relevant equipment.
It is widespread practice for classifying data in statistics according to different measuring levels. The levels of measurement usually give the basis of how a data should be summarized and presented. Considering this, there are four main types of levels of measurements considered in statistics which are: nominal, ordinal, interval, and ratio. The first level of measurement, nominal-level data is the lowest and most primitive among the four and it represents data collected in the form of labels or names. Such data in the nominal form usually lacks order and can only be classified or counted. An example of nominal data is whereby samples of fruits are taken which have varying colors like orange, yellow, red, green, and brown (Lind et. Al., 2015). After taking such data, there is no order required in labeling the assorted colors and any of them can be recorded first.
The second level of measurement, ordinal-level data, is based on the relative ranking or rating of items based on a defined attribute or qualitative attribute. The variables here are either ranked or counted. For example, a sample of ten countries may be ranked based on the coldest to the warmest country to establish the most suitable destination for summer tourism. The interval-level of measurement is the third type and is higher than the former two discussed. It usually is based on scale measurements whereby the units of measurement are known. An example of this is the measurement of temperature ranges in degrees Celsius as this may be determined to run in a certain scale like 0-100 (Lind et. Al., 2015). The final level of measurement, the ratio-level data, is the highest among the four. The ratio-level is based on a scale with a known unit of measurement which has a meaningful interpretation of the zero on the scale. There are many examples of this level of measurements such as height, weight, units of production, and distance.
References
Lind, D., Marchal, W., & Wathen, S.A. (2015). Statistical techniques in business and economics (16th ed.). McGraw-Hill.

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