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If your p-value meets your significance level requirements, then your alternative hypothesis may be valid and you may reject the null hypothesis. A low p-value offers stronger support for your alternative hypothesis. While the alpha is the significance level you're trying to achieve, the p-level is what your actual data is showing when you calculate it. The p-value, or calculated probability, indicates the probability of achieving the results of the null hypothesis. However, you'll also have a bigger chance at being wrong about your conclusion.
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If you set a high alpha (0.25), then you'll have a better shot at supporting your alternative hypothesis, since you don't need to find as big a difference between your test groups. In other words, the significance level is a statistical way of demonstrating how confident you are in your conclusion. If you set the alpha at 0.05, then there is a 5% chance you'll find support for the alternative hypothesis (thus rejecting the null hypothesis) when, in truth, the null hypothesis is actually true and you were wrong to reject it. You may also see 0.1 or 0.01, depending on the area of study. A typical significance level is set at 0.05 (or 5%). It defines the probability that the null hypothesis will be rejected. This is the determiner, also known as the alpha (α). By rejecting the null hypothesis, you accept the alternative hypothesis.
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This is the opposite of the null hypothesis, demonstrating or supporting a statistically significant result. You'll want to prove an alternative hypothesis. Researchers work to nullify or disprove null hypotheses. It's the least exciting result, showing no significant difference between two or more groups. It's the default, or what we'd believe if the experiment was never conducted. The null hypothesis is a commonly accepted fact. These are the steps you'll want to take to see if your suppositions stand up: Remember, a hypothesis is a statement regarding what you believe might happen. At this point, you'll already have a hypothesis ready to go.