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.
Choice A is the only one that is changing at a constant rate. The reason is that for all three other choices the new rate is based on a different amount after the percent has been applied or the doubling of the ants has been applied. The price per day for the lunch Is constant because whatever the number of days is the amount would always stay the same for each of those days.
