# Hypothesis Testing

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Science Strategy

A hypothesis is a proposed explanation for a particular phenomenon, which can be tested using a variety of methods. Hypothesis testing, a key component of inferential statistics, helps demonstrate the statistical significance between two tested groups. This process involves the creation of two opposing hypotheses: the null hypothesis and the alternative hypothesis. The null hypothesis posits that there is no significant statistical difference between the tested groups, whereas the alternative hypothesis contends that there is a significant statistical difference.

To measure the statistical significance of the hypothesis test, researchers use an alpha level, often set at 0.05, which indicates the probability of rejecting the null hypothesis when it is true - a scenario known as a false positive. If a more stringent alpha level of 0.01 is used, more data is required in order to reach this level of statistical significance. To understand the precision of the result or the likelihood of the same outcome if the study was to be repeated, the confidence interval (CI) is calculated. For an alpha level of 0.05, the CI would be 95%. The decision to accept or reject the null hypothesis is also influenced by the p-value, which must be lower than the predetermined alpha value for the null to be rejected.

In some instances, studies may either aim to show that one element is superior to another (one-sided superiority test), or that one element is not significantly worse than another (one-sided non-inferiority test). A third construct, the equivalence test, seeks to show that one element is equal, or nearly equal, to another. However, errors can occur during hypothesis testing. A Type I Error is a false positive - incorrectly rejecting the null hypothesis when it is true; while a Type II Error is a false negative, where the alternative hypothesis is wrongly rejected. The likelihood of committing a Type II error is represented by beta (β), which also shows the area of overlap between the null and alternative distribution curves. The less overlap, the more statistical power, or ability to detect a difference, exists. This power can be approximately defined as 1-β.

Lesson Outline

<ul> <li>Definition of a Hypothesis</li> <ul> <li>Proposed explanation for a particular phenomenon</li> </ul> <li>Hypothesis Testing</li> <ul> <li>Key component of inferential statistics</li> <li>Demonstrates statistical significance between two tested groups</li> <li>Involves creation of null and alternative hypotheses</li> <li>Null Hypothesis: No significant statistical difference between groups</li> <li>Alternative Hypothesis: Significant statistical difference between groups</li> </ul> <li>Measurement of Statistical Significance</li> <ul> <li>Utilization of alpha level (often set at 0.05)</li> <li>Alpha level indicates probability of rejecting null hypothesis when true (false positive)</li> <li>If using a stricter alpha level (e.g., 0.01), more data is required to reach statistical significance</li> </ul> <li>Confidence Interval (CI)</li> <ul> <li>Measures precision of result or likelihood of same outcome if study is repeated</li> <li>For an alpha level of 0.05, the CI is 95%</li> </ul> <li>P-Value</li> <ul> <li>Influences decision to accept or reject null hypothesis</li> <li>Should be lower than predetermined alpha value to reject null hypothesis</li> </ul> <li>Types of Studies</li> <ul> <li>One-sided superiority test: one element higher than the other</li> <li>One-sided non-inferiority test: one element not lower than the other</li> <li>Equivalence test: one element equal or nearly equal to another</li> </ul> <li>Errors in Hypothesis Testing</li> <ul> <li>Type I Error: False positive (incorrectly rejecting true null hypothesis)</li> <li>Type II Error: False negative (wrongly rejecting alternative hypothesis)</li> </ul> <li>Beta (β) and Statistical Power</li> <ul> <li>Beta represents likelihood of committing a Type II error</li> <li>Indicates area of overlap between null and alternative distribution curves</li> <li>Less overlap means more statistical power (ability to detect difference)</li> <li>Statistical power can be roughly defined as 1-β</li> </ul> </ul> Don't stop here!

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FAQs

## What is hypothesis testing and how does it relate to statistical significance?

Hypothesis testing is a statistical method used to make inferences about population parameters based on a sample of data. It involves stating explicit hypotheses, a null hypothesis (H0) that represents the existing belief (e.g., no effect, no difference), and an alternative hypothesis (H1), which represents the effect or difference of interest. Statistical significance is determined by comparing the probability of the observed data under the null hypothesis to a chosen significance level (alpha level, usually set at 0.05). If this probability (the p-value) is less than the alpha level, the null hypothesis is rejected, and the results are considered statistically significant, supporting the alternative hypothesis.

## How do I interpret a p-value in the context of hypothesis testing?

The p-value represents the probability of obtaining the observed data (or more extreme data) if the null hypothesis is true. A low p-value (typically less than the alpha level, e.g., 0.05) indicates that the observed data is unlikely to have occurred by chance alone, and thus, the null hypothesis is rejected in favor of the alternative hypothesis. In other words, a low p-value indicates evidence against the null hypothesis. Conversely, a high p-value suggests that the observed data is consistent with the null hypothesis, and there is insufficient evidence to reject it.

## What is the purpose of a confidence interval in hypothesis testing?

A confidence interval is a range of values that estimates the true population parameter with a certain level of confidence, such as 95%. It provides an interval estimate of the parameter of interest (e.g., population mean, proportion, difference, etc.) rather than a point estimate from the sample. In hypothesis testing, the confidence interval can be used to evaluate the null hypothesis. If the interval does not contain the value specified in the null hypothesis, then there is evidence against the null hypothesis at the specified confidence level. Confidence intervals give additional information about the precision of the estimate and the likely range of the true population parameter.

## How do I choose an appropriate alpha level (significance level) for hypothesis testing?

The alpha level, also known as the significance level, is chosen before conducting a hypothesis test and represents the probability of rejecting the null hypothesis when it is actually true (Type I error). A common choice is an alpha level of 0.05, which corresponds to a 5% chance of making a Type I error. The choice of alpha level depends on the context and the consequences of making such an error. In situations where the cost of a Type I error is high, a lower alpha level (e.g., 0.01) is chosen to reduce the risk of falsely rejecting the null hypothesis. On the other hand, in situations where a Type I error is less critical, a higher alpha level (e.g., 0.10) might be used to increase the power of the test, making it more likely to detect a true effect.