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All of the hypothesis tests we have considered up to this point have been two-sided tests. Unlike the p-value, the level is not derived from any observational data and does not depend on the underlying hypothesis the value of is instead set by the researcher before examining the data. A hypothesis test which is designed to identify a difference from a hypothesized value in either direction is called a two-sided test.
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The null hypothesis is rejected if any of these probabilities is less than or equal to a small, fixed but arbitrarily pre-defined threshold value, which is referred to as the level of significance. A test of statistical hypothesis where the null hypothesis ( H 0 ) (H0) ( H 0 ) is H 0 : 0 H0:\mu\mu0 H 0 : 0 against the alternative hypothesis H 1. The smaller the p-value, the higher the significance because it tells the investigator that the hypothesis under consideration may not adequately explain the observation. Two-sided hypothesis test is also famous as a non-directional test or a two-tailed hypothesis test because two-sided test is used to test effect on both the directions. Depending on how it is looked at, the "more extreme than what was actually observed" can mean (right-tail event) or (left-tail event) or the "smaller" of and (double-tailed event). The p-value is defined as the probability, under the null hypothesis, here simply denoted by (but is often denoted, as opposed to, which is sometimes used to represent the alternative hypothesis), of obtaining a result equal to or more extreme than what was actually observed. Interval is: B1 +- 2 x SE(B1) Interval is: -0.3, 0.13 I would say that the result here is 0.13 because that is the bigger number where we still CAN NOT reject the null hypothesis. The p-value or probability value or asymptotic significance 1