In another section we present some basic test statistics to evaluate a hypothesis. Hypothesis testing generally uses a test statistic that compares groups or examines associations between variables. When describing a single sample without establishing relationships between variables, a confidence interval is commonly used. The p-value describes the probability of obtaining a sample statistic as or more extreme by chance alone if your null hypothesis is true.
This p-value is determined based on the result of your test statistic. Your conclusions about the hypothesis are based on your p-value and your significance level. Cautions About P-Values Your sample size directly impacts your p-value. Large sample sizes produce small p-values even when differences between groups are not meaningful.
You should always verify the practical relevance of your results. On the other hand, a sample size that is too small can result in a failure to identify a difference when one truly exists. Plan your sample size ahead of time so that you have enough information from your sample to show a meaningful relationship or difference if one exists. See calculating a sample size for more information.
If you do a large number of tests to evaluate a hypothesis called multiple testing , then you need to control for this in your designation of the significance level or calculation of the p-value. For example, if three outcomes measure the effectiveness of a drug or other intervention, you will have to adjust for these three analyses.
Hypothesis testing is not set up so that you can absolutely prove a null hypothesis. Therefore, when you do not find evidence against the null hypothesis, you fail to reject the null hypothesis. When you do find strong enough evidence against the null hypothesis, you reject the null hypothesis. Your conclusions also translate into a statement about your alternative hypothesis. When presenting the results of a hypothesis test, include the descriptive statistics in your conclusions as well.
Report exact p-values rather than a certain range. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.
In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.
To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. A survey involves asking a series of questions to a random population sample and recording self-reported responses. Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention. Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.
Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions. Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used and the reason for the analysis. Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data.
Such data may come from a larger population, or from a data-generating process. The word "population" will be used for both of these cases in the following descriptions. In hypothesis testing, an analyst tests a statistical sample, with the goal of providing evidence on the plausibility of the null hypothesis. Statistical analysts test a hypothesis by measuring and examining a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses: the null hypothesis and the alternative hypothesis.
The null hypothesis is usually a hypothesis of equality between population parameters; e. The alternative hypothesis is effectively the opposite of a null hypothesis e. Thus, they are mutually exclusive , and only one can be true. However, one of the two hypotheses will always be true.
All hypotheses are tested using a four-step process:. A random sample of coin flips is taken, and the null hypothesis is then tested. If, on the other hand, there were 48 heads and 52 tails, then it is plausible that the coin could be fair and still produce such a result. In cases such as this where the null hypothesis is "accepted," the analyst states that the difference between the expected results 50 heads and 50 tails and the observed results 48 heads and 52 tails is "explainable by chance alone.
Trading Basic Education. Tools for Fundamental Analysis. Advanced Technical Analysis Concepts.
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