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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.
● 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
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.
Hypothesis testing was developed in the early 20th century by statisticians such as Ronald Fisher, Jerzy Neyman and Egon Pearson. These methods were created to support scientific research by providing objective procedures for testing theories and hypotheses.
Hypothesis tests are essential in many fields such as medicine, psychology, social sciences and economic research. They are used to test the effectiveness of treatments, evaluate differences between groups and analyze trends in data.
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