Hypothesis testing in SPSS anaysis

Hypothesis testing was introduced by Ronald Fisher, Jerzy Neyman, Karl Pearson and Pearson’s son, Egon Pearson. Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. Hypothesis Testing is basically an assumption that we make about the population parameter.

Key terms and concepts:
Null hypothesis: Null hypothesis is a statistical hypothesis testing that assumes that the observation is due to a chance factor. In hypothesis testing, null hypothesis is denoted by; H0: μ1 = μ2, which shows that there is no difference between the two population means.

Alternative hypothesis: In hypothesis testing, alternative hypothesis, contrary to the null hypothesis, shows that observations are the result of a real effect.

Level of significance: In hypothesis testing, the level of significance refers to the degree of significance in which we accept or reject the hypothesis. In hypothesis testing, 100% accuracy is not possible for accepting or rejecting a hypothesis. So, we therefore select a level of significance that is usually 5%.

Type I error: In hypothesis testing, there are two types of errors. The first is type I error and the second is type II error. In Hypothesis testing, type I error occurs when we are rejecting the null hypothesis, but that hypothesis was true. In hypothesis testing, type I error is denoted by alpha. In Hypothesis testing, the normal curve that shows the critical region is called the alpha region.

Type II errors: In hypothesis testing, type II errors occur when we accept the null hypothesis but it is false. In hypothesis testing, type II errors are denoted by beta. In Hypothesis testing, the normal curve that shows the acceptance region is called the beta region.

Power: In Hypothesis testing, power is usually known as the probability of correctly accepting the null hypothesis. In hypothesis testing, 1-beta is called power of the analysis.

One-tailed test: In hypothesis testing, when the given statistical hypothesis is one value like H0: μ1 = μ2, it is called the one-tailed test.

Two-tailed test: In hypothesis testing, when the given statistics hypothesis assumes a less than or greater than value, it is called the two-tailed test.

Statistical decision for hypothesis testing:
In statistical analysis, we have to make decisions about the hypothesis. These decisions include deciding if we should accept the null hypothesis or if we should reject the null hypothesis. Every test in hypothesis testing produces the significance value for that particular test. In Hypothesis testing, if the significance value of the test is greater than the predetermined significance level, then we accept the null hypothesis. If the significance value is less than the predetermined value, then we should reject the null hypothesis. For example, in Hypothesis testing, if we want to see the degree of relationship between two stock prices and the significance value of the correlation coefficient is greater than the predetermined significance level, then we can accept the null hypothesis and conclude that there was no relationship between the two stock prices. However, due to the chance factor, it shows a relationship between the variables.