WEEK 1: WHAT ARE THE CHANCES OF HAPPENING? Flashcards
Describe the process of hypothesis testing.
*Pose a question
*Create a hypothesis
*Make predictions
*Design & carry out experiments to test your predictions
*Analyse your data using statistics
What is null hypothesis?
-always states that there is no differences between groups.
-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 try to prove did not happen.
-use statistical analysis to either accept or reject the null hypothesis.
What is Research or Alternative hypothesis?
States that there is a difference between groups
Can accept or reject it
What is a probability value?
What is the standard p-value?
*Is a number describing how likely it is that your data would have occurred by random chance
(i.e., that the null hypothesis is true).
*0.05
What if P value is more than 0.05?
A p-value more than the significance level (typically p > 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.
*It means that the observed data do not provide strong enough evidence to reject the null hypothesis.
what if P value is less than 0.05?
The smaller the p-value the less likely the results occurred by random chance, and the stronger the evidence that you should reject the null hypothesis.
A p-value less than or equal to your significance level (typically ≤ 0.05) is statistically significant.
This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis; we can only reject it or fail to reject it.
How does sample size affect the interpretation of p-values?
With a larger sample, even small differences between groups or effects can become statistically significant, yielding lower p-values. In contrast, smaller sample sizes may not have enough statistical power to detect smaller effects, resulting in higher p-values.
Therefore, a larger sample size increases the chances of finding statistically significant results when there is a genuine effect, making the findings more trustworthy and robust.
What is type I error?
A Type I error is when we reject the Null hypothesis but we are wrong, there is not a real difference.
Someone tells you that the null hypothesis is TRUE but we DISAGREE with them.
What is type II error?
A Type II error is when we wrongly accept the Null hypothesis.
Someone tells you that the null hypothesis is FALSE, but we believe that. it is TRUE.
