Statistics At A Glance

STATISTICS AT A GLANCE

By: Julhusin B. Jalisan

The growing complexity of human endeavors in recent years has definitely increased the importance of statistics. Businessmen and economists consider statistics as a vital tool in planning and controlling production and distribution of goods and services, in formulating policies, and in forecasting business trends. Farmers and fishermen knowingly or unknowingly use statistics in comparing their outputs as affected by various factors. Similarly, the teachers make use of statistics in the computation and analysis of the students' academic performance. Indeed, the use of statistics is boundless. It is, therefore, imperative for every so-called literate to understand the rudiments of statistics in order to be more sensitive of the various issues and problems in our midst.

The word "statistics" comes in various definitions, but not one could be regarded as complete and absolute. It has been frequently referred to either as the quantitative or numerical information itself or the methods of dealing with the information. To facilitate understanding, many statisticians prefer the term "statistical data" for quantitative information and the methods of dealing with the information the "statistical methods."

Statistical Data

Quantitative or numerical information may be found almost everywhere. However, not all quantitative information is regarded as statistical data. Quantitative information suitable for statistical analysis must be a set of numbers that are measurable and show significant relationships. In other words, statistical data are numbers that can be subjected to comparison, analysis, and interpretation.

The area from which statistical data are collected is generally referred to as the population or universe. A population can be finite or infinite. A finite population has a limited number of objects or cases, whereas an infinite population has an unlimited number.

The task of collecting data from a small finite population is relatively simple. If it is desired to obtain a complete set of data on the monthly incomes of the college instructors in a university, we may simply ask each instructor his/her monthly income. However, collecting such data from a large population is sometimes impractical or nearly impossible.

In order to avoid the impractical or impossible task, a sample consisting of a group of representative items is usually drawn from the population. The sample is then used for statistical study and the findings from the sample are used as the basis to describe, estimate, or project the characteristics of the population.

Statistical Methods

Statistical methods are usually divided into five basic steps:

1. Collection. Quantitative information supplies facts for solving problems involving numbers. After the identification of the problem, certain relevant facts that can be expressed numerically should be gathered. Statistical data can be classified, according to source, as primary and secondary data. The latter is the most convenient and economic way of obtaining information from published materials. When published data are not available for a particular study, primary data may become necessary. Collecting primary data by survey is usually a costly, tedious, and time-consuming process.

2. Organization. Secondary statistical data are usually in organized form. However, those that are collected from a survey need organization. The first step in organizing data is editing. This is done for the purpose of correcting omissions, inconsistencies, irrelevant answers, and erroneous computations. The next step is classifying, which is to decide the proper classifications in which the edited items will be grouped. This is a very important step since the succeeding steps are affected by given classifications. The last step is tabulation. In this step, similar items are counted and recorded according to proper classifications.

3. Presentation. In order to facilitate statistical analysis, data are presented in textual form, table, or graph. Textual presentation is convenient only for presenting a few items. When a large mass of data is involved, this becomes inefficient and burdensome, since detailed explanations and properties of data may have to be repeated many times. Users usually prefer Tables if they can be effectively constructed. A graph is a pictorial representation of data. It usually gives the user of data only an approximate value of the facts.

4. Analysis. This is about the analysis of a population or universe based on a sample study. There are numerous methods of analyzing statistical data. Some are simple observation, while others necessitate the use of sophisticated and highly mathematical tools.

5. Interpretation. After the completion of the analysis, the findings must be interpreted. Correct interpretation will lead to a valid conclusion of the study.

GIGO: Garbage In,