We define statistics as a branch of mathematics as a means to analyze, understand what we observe and explain synopses, so as to create sense and meaning of our explanations and observations. Each day in life we come across information that originates in various forms. This is to means in order to put sense in this information; there is the necessity to use statistics. Though, due to its empirical applications and focus, statistics is typically regarded as a distinctive math’s sciences and not only a math’s branch (Chance et al, 2005) Therefore, in certain tasks a statistician use is less mathematical; for example, ensuring that collection of data is carried out in a way that yields effective deductions, reportage of results/coding statistical data in ways logical to the users. Statistics is recognized to advance the quality of data by shaping specific survey experiment designs and samples. It provides tools used to forecast and utilize data as well as the models for statistics. Also, it’s pertinent in many hypothetical fields that comprise the government, business, social and natural disciplines.
Descriptive or expressive statistics are exclusively used to designate the sample example under study. They are essentially used to describe the important features of a given data. They provide simple summaries concerning the measures and the samples. When utilized together with simple graphics study, they form the heart of practically each quantitative study of data. This means that descriptive statistics both made use of graphical and numerical summaries to explain a given data. Numerical summaries which can either measure the central tendency of a given set of data or which describe the spread of a given data. They use numbers like median, mean and mode to indicate the center or average of the data under observation. Additionally, standard deviation and range are used to specify how the data is spread. In more complex statistics, regression and correlation are used to describe paired data (Laverick et al, 2004).
Inferential statistics entails making some conclusions from a given data. Thus, this statistics attempts to test a given hypothesis and drawing conclusions concerning a population, founded on the sample where it was collected. This means that with inferential statistics, one attempts to reach conclusions, which extend past the immediate data alone. It permits one to use samples to make generalizations concerning the populations from where the samples were drawn. Thus, it is paramount that the sample accurately represents the population under study. These statistics depends on the use of random sampling method that makes sure that a sample is a characteristic of the whole populace under study. A true sample in a given study demonstrates that everybody in the populace has an identical chance of getting chose for the sample. This statistics take into consideration the sampling error in the sample collected (Kolaczyk, 2009).