In hypothesis testing, the test of hypothesis is developed assuming the ALTERNATIVE hypothesis is true.

	In hypothesis testing, the test of hypothesis is developed assuming the ALTERNATIVE hypothesis is true.
	In hypothesis testing, the NULL hypothesis states that a parameter of interest is equal to a specific value.
	For larger sample sizes (n>30) with the STANDARD DEVIATION KNOWN, the t-distribution should be used to test hypotheses about the population mean.
	In hypothesis testing, the ALTERNATIVE hypothesis states how the parameter of interest differs from the null hypothesis value.
	The p-value is determined by the ALTERNATIVE hypothesis.
	Type I error can be defined as the probability the null hypothesis is true.
	Type II error is the probability of failing to reject H0 when it is false.
	The SMALLER the significance level, the stronger the evidence needed to reject H0.
	The SIGNIFICANCE LEVEL determines how unusual an outcome must be in order to reject or fail to reject the null hypothesis.
	The p-value is the probability the null hypothesis is true.

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