COHP 350 Quiz 1 Study Set Questions And Answers
What is the difference between descriptive and inferential statistics?
Descriptive statistics
Descriptive statistics is a field that characterizes data quantitatively. It only describes
specific people or items that are evaluated. It is useful for small initiatives, but the findings
cannot be generalized to a broad population. In descriptive statistics, we can select a sample
of interest and analyse the data to illustrate its attributes. To describe a specific circumstance,
It provides detailed information about the identified information and summarizes the data
samples. The statistical measures that are commonly used in descriptive statistics are value of
kurtosis, scattering, and internal consistency. Analyze the data collection's center using the
mean and standard deviation to determine its central tendency. Dispersion: How far away
from the heart do the data extend? The wide variety or sample variance can be used to
quantify dispersion. Virtues are more attentively clustered all around center when the
dispersion is low. Measured values that are spread farther from the center have higher
dispersion. The frequency distribution can also be graphed, Skewness: It aids in determining
if a distribution is symmetric or skewed (Stapor, 2020).
Descriptive statistics are important because raw data alone, especially if there is a lot
of it, makes it hard to comprehend what the data is showing. Descriptive statistics enable us
to clearly present the data, which facilitates data interpretation. For illustration, if we had the
outcomes of 100 pieces of curriculum, we could be able to determine the significance of
those students' accomplishments from them. The dispersion or spread of the marks really
would be of interest to us. We are able to do this with descriptive statistics. An interesting
issue covered in other Laerd Official data guides is how to effectively describe data using
facts and figures and graphs (Kaur, Stoltzfus, & Yellapu, 2018).
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