Welcome back! In this installment, we'll pick up from where we left off. This segment is designed to be completed in approximately 10 minutes.
In our last post, we covered several essential aspects of hypothesis testing:
Parameter estimation
Low probability events and significance levels (commonly α = 0.05)
Comparison between parametric and non-parametric hypothesis testing.
Now, let's move forward from the previous section. Still focus on the requisition knowledge about hypothesis testing.
1.Setting Hypotheses
In "2.4 Sample vs. Population: Inferential Statistics (3) (σ² Known)," we reviewed the basic steps for conducting a hypothesis test.
Step 1. State the Hypothesis
Step 2. Choose the Significance Level and Appropriate Test
Step 3. Calculate the Test Statistic
Step 4. Make a Decision
2.Choosing the Right Hypothesis
However, if you want to take a basic step, you must select the appropriate hypothesis. This means we must consider the condition of both the sample data and the population data situation.
Along with the post "Four criteria determine which hypothesis test is appropriate to use", you can grasp this briefly. We outlined the criteria for selecting the appropriate hypothesis based on whether the data pertains to a sample or a population, thereby clarifying the decision-making process.
3.Type I and Type II Errors
We examined the implications of type I and type II errors in hypothesis testing, utilizing a vivid example to emphasize the potential consequences of erroneous conclusions. "2.5 Type I and Type II Errors in Hypothesis Testing" elaborates all these well , involves:
When to use Z-test vs T-test ?
Difference Between One-Sample Z Test vs T Test Formula
Definitions of both Type I error (α) and Type II error (β)
4.Understanding Hypothesis Types: One-tailed or Two-tailed test
This discussion included the differentiation between one-tailed and two-tailed hypothesis tests, detailing the contexts in which each type is employed, and distinguishing between right-tailed and left-tailed tests according to their respective validation purposes.
5.Realistic examples in hypothesis Testing
Thus, using the methodologies mentioned earlier, we employed numerous classic examples to illustrate all academic information in scenarios that are as realistic as possible, whether in life or business.
Example 1: Hypothesis Testing for the Population Mean (σ² Known)
Example 2: Hypothesis Testing for the Population Mean (σ² Unknown)
Example 4: A Left-tailed test example
Example 5: A Right-tailed test example
Above all, we discussed the content regarding the hypothesis testing for one population and the necessary prerequisite knowledge. Now, let us delve into specific cases of hypothesis testing for the means of two populations under varying data conditions.
Ow! And the post will emphasize top-notch data quality.
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