The alternative hypothesis states whether the population parameter differs from the value of the population parameter stated in the conjecture. In practice, the significance level is stated in advance to determine how small the p-value must be in order to reject the null hypothesis. Because different researchers use different levels of significance when examining a question, a reader may sometimes have difficulty comparing results from two different tests.
P-values provide a solution to this problem. For example, suppose a study comparing returns from two particular assets was undertaken by different researchers who used the same data but different significance levels. The researchers might come to opposite conclusions regarding whether the assets differ. To avoid this problem, the researchers could report the p-value of the hypothesis test and allow the reader to interpret the statistical significance themselves. This is called a p-value approach to hypothesis testing.
An independent observer could note the p-value, and decide for themself whether that represents a statistically significant difference or not. To determine this, the investor conducts a two-tailed test.
The p-value hypothesis test does not necessarily make use of a pre-selected confidence level at which the investor should reset the null hypothesis that the returns are equivalent.
Instead, it provides a measure of how much evidence there is to reject the null hypothesis. The smaller the p-value, the greater the evidence against the null hypothesis. Thus, if the investor finds that the p-value is 0. Although this does not provide an exact threshold as to when the investor should accept or reject the null hypothesis, it does have another very practical advantage.
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At this point, a word about error. Type I error is the false rejection of the null hypothesis and type II error is the false acceptance of the null hypothesis. As an aid memoir: think that our cynical society rejects before it accepts. The significance level alpha is the probability of type I error. The power of a test is one minus the probability of type II error beta.
Power should be maximised when selecting statistical methods. If you want to estimate sample sizes then you must understand all of the terms mentioned here. If you are interested in further details of probability and sampling theory at this point then please refer to one of the general texts listed in the reference section.
You must understand confidence intervals if you intend to quote P values in reports and papers. Statistical referees of scientific journals expect authors to quote confidence intervals with greater prominence than P values.
Saul McLeod , published When you perform a statistical test a p -value helps you determine the significance of your results in relation to the null hypothesis. The null hypothesis states that there is no relationship between the two variables being studied one variable does not affect the other. It states the results are due to chance and are not significant in terms of supporting the idea being investigated.
Thus, the null hypothesis assumes that whatever you are trying to prove did not happen. The alternative hypothesis is the one you would believe if the null hypothesis is concluded to be untrue. The alternative hypothesis states that the independent variable did affect the dependent variable, and the results are significant in terms of supporting the theory being investigated i.
A p-value, or probability value, is a number describing how likely it is that your data would have occurred by random chance i.
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