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What is Hypothesis Testing?
Hypothesis testing evaluates whether or not a particular hypothesis regarding the presence of an association between two variables is true. As such, hypothesis testing uses statistical norms to determine whether or not there’s evidence that supports a certain relationship (i.e. an effect or a relationship between two variables).
Hypothesis testing is particularly useful when quantitative data (i.e. numerical data) are analyzed. It’s used in a wide range of research methods including medical research, statistical studies, and qualitative studies.
What is the Purpose of Testing Hypotheses?
The purpose of applying the hypothesis test is to assess whether there are reasons to believe that a certain phenomenon exists or that there’s at least enough evidence on which to base an inference about its existence. When done in a correct and rigorous way, this process of finding out whether a relationship exists between variables or not can produce a large amount of useful knowledge and insight.
What is the Main Rule of Hypothesis Testing?
The main rule to follow when conducting hypothesis testing is to make sure that you correctly formulate your null hypothesis. You should do so before you begin your statistical analysis.
What is a Null Hypothesis?
It’s an attempt to evaluate how likely it is that your data are consistent with an effect or relationship between two variables.
Types Of Hypothesis
- Null Hypothesis: Is the opposite of the research claim, and it is assumed to be true until enough evidence shows that it is not.
- Alternative Hypothesis: The alternative hypothesis provides a way for the data to disprove what would be considered “true” if the null hypothesis was accepted as 100% accurate. The goal of statistical tests is to see if there are significant differences between what we observed in our sample and what would be predicted by the null hypothesis.
- Confidence Interval: A confidence interval is a range of values that include the mean value of all individuals in the sample. It is used as a “prediction” or as an “estimate” of what would be observed if there were no effect in the population from which our sample was taken. The goal of statistical tests is to see if there are significant differences between what we observed in our sample and what would be predicted by the null hypothesis.
- The P Value: A low P values means that the situation being studied by researchers is unlikely to be explained by chance. A high P values indicates greater chance that the results are due to sampling error, or some other factor. This post explains what this all means and how readers can better understand their findings.
- Significance Degree (alpha): Significance degree (alpha) determines how likely it is that results are due to chance alone; 0.05, 0.01 are common values used in research. The greater the statistical significance level, the more likely researchers are to reject the null hypothesis and conclude bias or error occurred in the experiment. A probability of 0.05 would mean that one is 95% confident that there is a difference in the results of our sample.
- Power: Power is the likelihood that a study will have enough groups to detect a significant difference between experimental groups and control groups observed with sample size (n). Power is often used in sample size determination.
A hypothesis is a testable statement about how the world works or what might happen in the future. It could be an idea, usually untested, that is formulated by a scientist or researcher and then tested with the help of others. Unfortunately, the number one reason for not performing tests on your hypotheses is because you don’t know where to start. This article will show you how to turn your ideas into experiments and even inspire new hypotheses from them!
The Importance of Testing Hypotheses
- Hypothesis is a theory that makes predictions that can be tested and are falsifiable.
- A hypothesis statement should make statements about the world in general, not just about the research. For example, if you have a hypothesis that “making decisions cause’s pain” then you must test whether or not this is true with other people who don’t have this hypothesis.
- A hypothesis can also be used as an explanation for existing data or as a way to explain how something works. For example, your hypothesis could explain why there is more revenue from selling a product in December than at other times of the year (“Christmas season boost revenue”). Or your hypothesis could explain why you are more likely to be fired for being rude or sarcastic in the workplace (“sarcasm is often a warning sign of poor leadership ability”).
- When explaining how something works, it is important to both make a statement about what does happen and what does not happen as well. For example, if you say that it takes 6 hours for an SUV to go 200 km on the highway then this implies that it would take 4 hours to get there if it was going 100 km/h and 8 hours if it was going 1 km/h. So make sure you test your hypothesis by testing both what happens and doesn’t happen.
- A hypothesis has a strong directional bias. This means that you should form your hypothesis before collecting data. For example, if you are making a sweater and want to increase revenue, it makes no sense to test if the sweater is black or blue before knowing whether you want to increase revenue or decrease costs.
Topics Studied Under Hypothesis Testing
- P-value calculation
- Null hypothesis rejection region: Rejection regions for a one-sided and two-sided test, respectively
- Hypothesis testing example (tapping method)
- Alternative hypothesis
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