Random sampling , sociology homework help

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zefpheerapr7613

Humanities

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Social Science Inquiry class

Assignments (2 – 5 pages per response is typical) – Define terms in “your own words, give practical, common-sense example of each, and use visual support

1. Describe and give clear, practical examples (charts, graphs, lists, etc.) the following concepts related to Statistical Inferences: random sampling; probability distribution; continuous probability distribution; expected value; normal distribution; type I errors; type II errors; t varaible/t score; null hypothesis; research hypothesis; alternative hypothesis; t test; chi square distribution

2. Describe the normal percentages of distribution in a normal curve (areas under the normal curve for various Z scores). What does that mean in regards to the normal distribution of one standard deviation from the mean, 2 standard devaiations, 3 standard deviations? Give a concrete example using social deviance as a topic.

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Running Head: SOCIAL SCIENCE

Social Science
Name
Institution Affiliation

2

SOCIAL SCIENCE
Q1
Random sampling is a selection technique where a group of individual are selected for a study
from a bigger strata. The person or strata chosen is entirely by coincidental and every individual
of the populace has an equal chance of being selected in the sample size (Ritchie, Lewis,
Nicholls, & Ormston, 2013). Suppose the government want to know the performance of
education in certain states.
Probability distribution is a statistical function having discrete variables which have integral
over any given interval.it is a probability which the random value is specified by variables lying
within the interval. The range is supposed to be between the maximum and minimum statistically
likely values. Plotting of likely values may be affected by various factors such as skewness,
kurtosis, distribution means and standard deviation.
Continuous probability distribution it is a probability distribution where the random value can
take any value. This because there are infinite values which can be assumed. Continuous
probability uses an equation known as probability density function (Seidman, 2013). The
equation used must fulfill these conditions the value of y must be greater than or equivalent to
zero for values of the second condition is y is supposed to be a function of x and the total area
under the curve is supposed to be equal to zero.
Expected value it is the predicted outcome of a study being conducted.
Normal distribution it i...


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