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Inferential statistics I Hypothesis Testing in the Basic Form of Conditional Probability / Bayes Rule in R Absolute Beginners Stratosphere by Steffen Schwerdtfeger Medical Student, Chart Universittsmedizin,
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01
Clearly state the null hypothesis and alternative hypothesis.
02
Determine the level of significance (alpha) to use.
03
Choose an appropriate test statistic based on the type of data and research question.
04
Collect and analyze data to calculate the test statistic.
05
Determine the p-value by comparing the test statistic to the probability distribution.
06
Make a decision to accept or reject the null hypothesis based on the p-value and level of significance.

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Researchers conducting experiments or studies in fields such as psychology, biology, economics, sociology, etc.
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Inferential statistics in hypothesis testing is a method of drawing conclusions about a population based on information from a sample.
Researchers, statisticians, or analysts who are conducting studies or experiments that require testing hypotheses.
To fill out inferential statistics in hypothesis testing, one must first formulate a null hypothesis and an alternative hypothesis, collect data, perform statistical tests, and interpret the results.
The purpose of inferential statistics in hypothesis testing is to make inferences about a population based on sample data and determine whether the null hypothesis can be rejected.
The information to be reported includes the hypotheses being tested, the data collected, the statistical tests performed, the results obtained, and the conclusions drawn.
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