Normal distribution, also called Gaussian distribution, [has a] familiar bell-shaped curve is ubiquitous in statistical reports, from survey analysis and quality control to resource allocation. The graph of the normal distribution is characterized by two parameters: the mean, or average, which is the maximum of the graph and about which the graph is always symmetric; and the standard deviation, which determines the amount of dispersion away from the mean.
In statistical studies, there is always the possibility that results that seem to support a hypothesis could have occurred purely by chance and actually have nothing to do with the hypothesis. In statistical jargon, the measure of this possibility is called the “P-value” which is an estimate of the probability that results occurred purely by chance. Researchers strive for a low P-value.
One of the most common errors we find in the press is the confusion between correlation and causation in scientific and health-related studies. ... But just because two things occur together does not mean that one caused the other, even if it seems to make sense. ... In general, it is extremely difficult to establish causality between two correlated events or observances. In contrast, there are many statistical tools to establish a statistically significant correlation.
To publish a statistics-based paper in a prestigious scientific journal, the results must be “statistically significant.” There are many ways, however, in which researchers can manipulate their data to meet the mathematical requirements for “statistical significance” and still be very wrong in their conclusions. ... The bottom line here is to always be somewhat suspicious of papers whose results depend upon statistical manipulation or modeling versus papers that present actual observations.
Employing statistics serves two purposes, (1) description and (2) prediction. Statistics are used to describe the characteristics of groups. These characteristics are referred to as variables. Data is gathered and recorded for each variable. Descriptive statistics can then be used to reveal the distribution of the data in each variable ... Prediction is a primary goal of inferential statistics.
There are two main branches of statistics: descriptive and inferential. Descriptive statistics is used to say something about a set of information that has been collected only. Inferential statistics is used to make predictions or comparisons about a larger group (a population) using information gathered about a small part of that population. Thus, inferential statistics involves generalizing beyond the data, something that descriptive statistics does not do.
In statistics, a variable has two defining characteristics:
1. A variable is an attribute that describes a person, place, thing, or idea.
2. The value of the variable can "vary" from one entity to another.
... Variables can be classified as qualitative (aka, categorical) or quantitative (aka, numeric).
Statistics: a set of concepts, rules, and procedures that help us to:
-organize numerical information in the form of tables, graphs, and charts
-understand statistical techniques underlying decisions that affect our lives and well-being
-make informed decisions
Statistics is a discipline that can help us resolve problems like making sense out of too much data. But, while statistical tools can be used to bring insight and clarity to large amounts of information, if used inappropriately, they can also lead to confusing, even misleading interpretations.
Statistics are all around us. In fact it would be difficult to go through a full week without using statistics. Imagine watching a football game where no one kept score. The action itself might provide enough excitement to hold your attention for a while, but think of all the drama that would be lost if winning and losing weren't at issue. ... Without statistics we couldn't plan our budgets, pay our taxes, enjoy games to their fullest, evaluate classroom performance... Are you beginning to get the picture? We need statistics.