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How do inferential statistics describe data differently than descriptive statistics?
Wiki User
∙ 14y ago
The term "descriptive statistics" generally refers to such information as the mean (average), median (midpoint), mode (most frequently occurring value), standard deviation, highest value, lowest value, range, and etc. of a given data set. It is a loosely used term, and not always meant to contrast with inferential statistics as the question implies.
But in the context of the question, descriptive statistics would be information that pertains only to the data that has actually been collected. In the case of an instructor calculating an average grade for a class, for example, the collected data would most likely be the only point of interest. Thus, descriptive statistics would be enough.
However, it is more common for a researcher to use a sample of collected data to make inferences and draw conclusions about a larger group (or "population") that the sample represents. For example, if you wanted to know the average age of users of this site, it would be unrealistic to question every singe user. So you might question a small sample and then extend that information to all users.
But if you found the average age in your sample to be 40, you could not immediately assume that 40 is the average for all users. You would need to use inferential statistics to calculate an estimate of how accurately your data represents the larger group. The most common way to do this is to calculate a standard error, which will produce a range within which the population average most likely (but not definitively) lies.
Therefore, in the simplest description (inferential statistics are also a part of much more powerful tests outside of this answer), descriptive statistics refer only to a sample while inferential statistics refer to the larger population from which the sample was drawn.
Wiki User
∙ 14y ago
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Deffirence of discriptive statistics to inferential statistics?
Descriptive statistics is the term given to the analysis of data that helps describe, show, or summarize data in a meaningful way such that patterns might emerge from the data. Inferential statistics are techniques that allow us to use population samples to make generalizations about the populations from which the samples were drawn.
What is a statistical problem?
The statistical problem helps to describe the whole issue of descriptive and inferential statistics. The main aspects of the statistical problems are the population should be clearly defined and also objectives.
What is descriptive mean?
descriptive statistics-quantitavely describe the main features of a collection of data. Descriptive statistics are distinguished from inferential.Statistics(or inductive statistics),in that descriptive statistics aim to summarize a data set,rather than use the data to learn about the population that the data are thought to represent.
Name and define the two areas of statistics?
Descriptive and Inferential:Descriptive statistics describe the data set.Inferential statistics use the data to draw conclusions about the population.
What are descriptive statistics used to describe?
They describe the basic features of data. They provide summaries about the sample and the measures, and together with simple graphic analysis, they form the basis of virtually every analysis of data.
Compare and contrast descriptive statistics and inferential statistics?
Descriptive statistics are meant to describe the situation such as the average or the range. Inferential statistics is used to differentiate between a couple of groups.
What is descriptive data?
Descriptive data is data that is used to summarize or describe samples of data. Descriptive data is different from inferential statistics because inferential statistics uses data to learn from it.
What are the division of statistics?
The division of statistics are generally divided into two groups: inferential and descriptive. Inferential statistics require that a conclusion is drawn from data, based almost solely on human inference. Descriptive statistics are numbers that describe a set of data.
What are similarities and difference between descriptive and inferential statistics?
Descriptive statistics describe the main features of a collection of data quantitatively. Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aim to summarize a data set quantitatively without employing a probabilistic formulation, rather than use the data to make inferences about the population that the data are thought to represent.
Deffirence of discriptive statistics to inferential statistics?
Descriptive statistics is the term given to the analysis of data that helps describe, show, or summarize data in a meaningful way such that patterns might emerge from the data. Inferential statistics are techniques that allow us to use population samples to make generalizations about the populations from which the samples were drawn.
What is a statistical problem?
The statistical problem helps to describe the whole issue of descriptive and inferential statistics. The main aspects of the statistical problems are the population should be clearly defined and also objectives.
What is descriptive mean?
descriptive statistics-quantitavely describe the main features of a collection of data. Descriptive statistics are distinguished from inferential.Statistics(or inductive statistics),in that descriptive statistics aim to summarize a data set,rather than use the data to learn about the population that the data are thought to represent.
What do statistical mean?
descriptive statistics-quantitavely describe the main features of a collection of data. Descriptive statistics are distinguished from inferential.Statistics(or inductive statistics),in that descriptive statistics aim to summarize a data set,rather than use the data to learn about the population that the data are thought to represent.
How can manager use statistics and what benefits he can get from the statistical methods?
Managers can apply some statistical technique to virtually every branch of public and private enterprise. These techniques are commonly separated into two broad categories: descriptive statistics and inferential statistics. Descriptive statistics are typically simple summary figures calculated from a set of observations. Suppose a professor computes an average grade for one accounting class. If the professor uses the statistic simply to describe the performance of that class, the result is a descriptive statistic of overall performance. Inferential statistics are used to apply conclusions about one set of observations to reach a broader conclusion or an inference about something that has not been directly observed. In this case, a professor might use the average grade from a series of previous accounting classes to estimate, or infer, the average grade for future accounting classes. Any conclusion made about future accounting classes is based solely on the inferential statistics derived from previous accounting classes. See the related link below for more information.
Name and define the two areas of statistics?
Descriptive and Inferential:Descriptive statistics describe the data set.Inferential statistics use the data to draw conclusions about the population.
What are descriptive statistics used to describe?
They describe the basic features of data. They provide summaries about the sample and the measures, and together with simple graphic analysis, they form the basis of virtually every analysis of data.
What are the major steps in social research?
By the time you get to the analysis of your data, most of the really difficult work has been done. It's much more difficult to: define the research problem; develop and implement a sampling plan; conceptualize, operationalize and test your measures; and develop a design structure. If you have done this work well, the analysis of the data is usually a fairly straightforward affair.In most social research the data analysis involves three major steps, done in roughly this order:Cleaning and organizing the data for analysis (Data Preparation)Describing the data (Descriptive Statistics)Testing Hypotheses and Models (Inferential Statistics)Data Preparation involves checking or logging the data in; checking the data for accuracy; entering the data into the computer; transforming the data; and developing and documenting a database structure that integrates the various measures.Descriptive Statistics are used to describe the basic features of the data in a study. 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. With descriptive statistics you are simply describing what is, what the data shows.Inferential Statistics investigate questions, models and hypotheses. In many cases, the conclusions from inferential statistics extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population thinks. 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.In most research studies, the analysis section follows these three phases of analysis. Descriptions of how the data were prepared tend to be brief and to focus on only the more unique aspects to your study, such as specific data transformations that are performed. The descriptive statistics that you actually look at can be voluminous. In most write-ups, these are carefully selected and organized into summary tables and graphs that only show the most relevant or important information. Usually, the researcher links each of the inferential analyses to specific research questions or hypotheses that were raised in the introduction, or notes any models that were tested that emerged as part of the analysis. In most analysis write-ups it's especially critical to not "miss the forest for the trees." If you present too much detail, the reader may not be able to follow the central line of the results. Often extensive analysis details are appropriately relegated to appendices, reserving only the most critical analysis summaries for the body of the report itself.
