In general, when the p-value is "high" (ie greater than or equal to the significance level), our sample results could have occurred by random chance alone when null hypothesis H0 is true. So we cannot reject H0 with high p-values. That doesn't mean we can accept H0 as being true. Statistical hypothesis testing is used to determine whether the result of a data set is statistically significant. This test provides a p-value, representing the probability that random chance could explain the result. In general, a p-value of 5% or lower is considered to be statistically significant. If the P-value is less, reject the null hypothesis. If the P-value is more, keep the null hypothesis. 0.003 < 0.05, so we have enough evidence to reject the null hypothesis and accept the claim.A null hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis. ... In the example, Susie's null hypothesis would be something like this: There is no statistically significant relationship between the type of water I feed the flowers and growth of the flowers.
You can combine like terms, which are terms that contain the same variables raised to the same power. That means you can combine the two . The other terms can't be combined.
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The other terms can't be combined.
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