Hypothesis Test

Hypotheses form assumptions about potential correlations within your data. By using hypothesis tests, you have the opportunity to check these assumptions statistically. They can be confirmed or rejected. Every hypothesis test is based on two fundamental concepts: the null hypothesis and the alternative hypothesis. Both are crucial in determining whether a hypothesis is confirmed or rejected.
As part of this process, you will become familiar with various test procedures that will enable you to analyze your data in a statistically sound way.

Quick Info

Contents

  • Basic population
  • Sample
  • Null hypothesis
  • Alternative hypothesis
  • Confidence interval
  • Confidence level
  • Significance level
  • Probability of error
  • p-value
  • Test for normal distribution
  • T-test & F-test
  • Analysis of variance
  • Test of proportions
  • Alpha error
  • Beta error
  • Error bar chart
  • Sample size
  • Selectivity
  • Effect
  • Practical examples
  • Duration:

Duration on request

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  • Individual Coaching
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Key information

Hypothesis tests are statistical procedures used to test assumptions about a population based on sample data. These tests allow researchers and analysts to make decisions about the validity of statistical hypotheses by calculating the probability with which the observed data would occur if the null hypothesis were true.

Beneftits

  • Well-founded decision-making
  • Minimize errors
  • Increase scientific credibility
  • Recognize (non-)random effects
  • Quantifying uncertainties

Risks

  • Incorrect interpretations
  • Overinterpretation of the p-value possible

With more than 4,000 projects and case studies, Alphadi is a leader in the field of Lean Six Sigma and Sales Process Engineering. We advise you holistically so that your goals are achieved sustainably and in the long term.

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