A result is called “statistically significant” whenever

A result is called "statistically significant" whenever the p-value is less than the predetermined significance level, which is often 0.05. This means that the probability of the observed data, given that the null hypothesis is true, is less than 5%. In other words, a statistically significant result is one that is very unlikely to have occurred if the null hypothesis were true. This gives strong evidence against the null hypothesis, so we reject the null hypothesis and accept the alternative hypothesis.

A result is called "statistically significant" whenever the p-value is less than the predetermined significance level, often 0.05. This means that the probability of the observed data, given that the null hypothesis is true, is less than 5%. In other words, if there is no real effect or difference, the observed data or more extreme would occur less than 5% of the time. This provides strong evidence against the null hypothesis, so we reject the null hypothesis and conclude that the observed data provide evidence for the alternative hypothesis.

A result is called "statistically significant" whenever the p-value is less than the predetermined significance level, which is often 0.05. This means that the probability of the observed data, given that the null hypothesis is true, is less than 5%. In other words, a statistically significant result is one that is very unlikely to have occurred by chance alone, suggesting that there is a relationship between the variables being studied.

A result is called "statistically significant" whenever the p-value is less than the predetermined significance level, which is often 0.05. This means that the probability of the observed data, given that the null hypothesis is true, is less than 5%. In other words, a statistically significant result is one that is very unlikely to have occurred if the null hypothesis were true. This provides strong evidence against the null hypothesis, so we reject it in favor of the alternative hypothesis.

A result is called "statistically significant" whenever the p-value is less than the predetermined significance level, often 0.05. This means that the probability of the observed data, given that the null hypothesis is true, is less than 5%. In other words, if there is no real effect or difference, the observed data is quite unlikely. Therefore, we reject the null hypothesis and conclude that there is a statistically significant effect or difference.

A result is called "statistically significant" whenever the p-value is less than the predetermined significance level, often 0.05. This means that the probability of the observed data, given that the null hypothesis is true, is less than 5%. In other words, if there is no real effect or difference, the observed data or more extreme would occur less than 5% of the time. This provides strong evidence against the null hypothesis, so you reject the null hypothesis and accept the alternative hypothesis.

A result is called "statistically significant" whenever the p-value is less than the predetermined significance level, which is often 0.05. This means that the probability of the observed data, given that the null hypothesis is true, is less than 5%. In other words, a statistically significant result is one that is very unlikely to have occurred if the null hypothesis were true. This provides strong evidence against the null hypothesis, and in favor of the alternative hypothesis.

A result is called "statistically significant" whenever the p-value is less than the predetermined significance level, which is often 0.05. This means that the probability of the observed data, given that the null hypothesis is true, is less than 5%. In other words, a statistically significant result is one that is very unlikely to have occurred if the null hypothesis were true. This gives strong evidence to reject the null hypothesis in favor of the alternative hypothesis.