Look at the answer Q: If False, correct it. Statistical interpretation depends not only upon statistical ideas but also upon "ordinary" clear thinking regarding ideas of cause and effect.
They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. Descriptive statistics are typically distinguished from inferential statistics.
With descriptive statistics you are simply describing what is or what the data shows.
With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data. Descriptive Statistics are used to present quantitative descriptions in a manageable form. In a research study we may have lots of measures.
Or we may measure a large number of people on any measure. Descriptive statistics help us to simplify large amounts of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary. For instance, consider a simple number used to summarize how well a batter is performing in baseball, the batting average.
This single number is simply the number of hits divided by the number of times at bat reported to three significant digits. A batter who is hitting. The single number describes a large number of discrete events. This single number describes the general performance of a student across a potentially wide range of course experiences.
Every time you try to describe a large set of observations with a single indicator you run the risk of distorting the original data or losing important detail. The batting average doesn't tell you whether the batter is hitting home runs or singles.
It doesn't tell whether she's been in a slump or on a streak. The GPA doesn't tell you whether the student was in difficult courses or easy ones, or whether they were courses in their major field or in other disciplines. Even given these limitations, descriptive statistics provide a powerful summary that may enable comparisons across people or other units.
Univariate Analysis Univariate analysis involves the examination across cases of one variable at a time. There are three major characteristics of a single variable that we tend to look at: The distribution is a summary of the frequency of individual values or ranges of values for a variable.
The simplest distribution would list every value of a variable and the number of persons who had each value.
For instance, a typical way to describe the distribution of college students is by year in college, listing the number or percent of students at each of the four years.
Or, we describe gender by listing the number or percent of males and females. In these cases, the variable has few enough values that we can list each one and summarize how many sample cases had the value. But what do we do for a variable like income or GPA?
With these variables there can be a large number of possible values, with relatively few people having each one.· What is one example of inferential statistics used in your workplace?
· How is data at each of the four levels of measurement used in your workplace? If your workplace does not use all four levels, describe how such data could be used. One must determine whether to code only from a pre-defined set of concepts and categories, or if one will develop some or all of these during the coding process.
For example, using a predefined set, Horton would code only for profane language.
What is one example of inferential statistics used in your workplace? How is data at each of the four levels of measurement used in your workplace? If your workplace does not use all four levels, describe how such data could be used. SAMPLING IN RESEARCH Many populations about which inferences must be made are quite large.
For example, Consider the population of high school seniors in . Statistics at the Work Place Statistics is a single item in a collection of items.
It is further defined as a branch of mathematics that is involved in collecting organizing and summarizing of data. Follow along with this worked out example of a hypothesis test so that you can understand the process and procedure.
Statistics Inferential Statistics Basics Tutorials Statistics Formulas Probability & Games Practice Problems Descriptive Statistics Applications of Statistics Hypothesis Testing With One-Sample t-Tests.