Descriptivestatistics are distinguished from inferential statistics in that descriptivestatistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent.
This generally means that descriptivestatistics, unlike inferential statistics, are not developed on the basis of probability theory.
Even when a data analysis draws its main conclusions using inferential statistics, descriptivestatistics are generally also presented.
DescriptiveStatisticsDescriptivestatistics provides simple summaries about the sample and about the observations that have been made.
Descriptivestatistics and inferential statistics are both important components of statistics when learning about a population.
Descriptivestatistics can be manipulated in many ways that can be misleading.
Statistical Bias Bias is another common distortion in the field of descriptivestatistics.
Exclusion bias arises due to the systematic exclusion of certain individuals from the study Limitations of DescriptiveStatisticsDescriptivestatistics is a powerful form of research because it collects and summarizes vast amounts of data and information in a manageable and organized manner.
To illustrate you can use descriptivestatistics to calculate a raw GPA score, but a raw GPA does not reflect: how difficult the courses were, or the identity of major fields and disciplines in which courses were taken.
In other words, every time you try to describe a large set of observations with a single descriptivestatistics indicator, you run the risk of distorting the original data or losing important detail.
Descriptivestatistics can be manipulated in many ways that can be misleading, including the changing of scale and statistical bias.
Statistics also provides tools for prediction and forecasting.
This is called descriptivestatistics .
Descriptivestatistics and analysis of the new data tend to provide more information as to the truth of the proposition.The Statistical Process When applying statistics to a scientific, industrial, or societal problems, it is necessary to begin with a population or process to be studied.
Descriptivestatistics summarize the population data by describing what was observed in the sample numerically or graphically.
Probability is used in "mathematical statistics" (alternatively, "statistical theory") to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures.
Statistics deals with all aspects of the collection, organization, analysis, interpretation, and presentation of data.
Statistics that describe or summarize can be produced for quantitative data and to a lesser extent for qualitative data.
Therefore, all descriptivestatistics can be calculated using quantitative data.
As qualitative data represent individual (mutually exclusive) categories, the descriptivestatistics that can be calculated are limited, as many of these techniques require numeric values which can be logically ordered from lowest to highest and which express a count.
Plots play an important role in statistics and data analysis.
Histogram: In statistics, a histogram is a graphical representation of the distribution of data.
Box plot: In descriptivestatistics, a boxplot, also known as a box-and-whisker diagram, is a convenient way of graphically depicting groups of numerical data through their five-number summaries (the smallest observation, lower quartile (Q1), median (Q2), upper quartile (Q3), and largest observation).
Distribution Shapes In statistics, distributions can take on a variety of shapes.
Considerations of the shape of a distribution arise in statistical data analysis, where simple quantitative descriptivestatistics and plotting techniques, such as histograms, can lead to the selection of a particular family of distributions for modelling purposes.