Basic Concepts of Inferential Statistics

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Basic Concepts of Inferential Statistics
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What is inferential statistics?

• Inferential statistics is a technique used to
  draw conclusions about a population by testing
  the data taken from the sample of that
  population.
• It is the process of how generalization from
  sample to population can be made. It is assumed
  that the characteristics of a sample is similar
  to the populations characteristics.
• It includes testing hypothesis and deriving
  estimates.
• It focuses on making statements about the
  population.

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The process of inferential analysis
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Sampling Methods

• Random sampling is the best type of sampling
  method to use with inferential statistics. It is
  also referred to as probability sampling.
• In this method, each participant has an equal
  probability of being selected in the sample.
• In case the population is small enough then
  everyone can be used as a participant.
• Another sampling technique is Snowball sampling
  which is a non-probability sampling.
• Snowball sampling involves selecting participants
  on the basis of information provided by
  previously studied cases. This technique is not
  applied for inferential statistics.

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Important Definitions

• Probability is the mathematical possibility that
  a certain event will take place. They can range
  from 0 to 1.00
• Parameters describe the characteristics of a
  sample of population. (Variables such as age,
  gender, income, etc.).
• Statistics describe the characteristics of a
  sample on the same types of variables.
• Sampling Distribution is used to make inferences
  based on the assumption of random sampling.

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Sampling Error Concepts

• Sampling Error Inferential statistics takes
  sampling error (random error) into account. It is
  the degree to which a sample differs on a key
  variable from the population.
• Confidence Level The number of times out of 100
  that the true value will fall within the
  confidence interval.
• Confidence IntervalA calculated range for the
  true value, based on the relative sizes of the
  sample and the population.
• Sampling error describes the difference between
  sample statistics and population parameters.

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Sampling Distribution Concepts
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types of hypotheses

• Alternative hypothesis It specifies expected
  relationship between two or more variables. It
  may be symbolized by H1 or Ha.
• Null hypothesis It is the statement that says
  there is no real relationship between the
  variables described in the alternative
  hypothesis.
• In inferential statistics, the hypothesis that is
  actually tested is the null hypothesis.
  Therefore, it is essential to prove that the null
  hypothesis is not valid and alternative
  hypothesis is true and should be accepted.

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Hypothesis Testing Process
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