WEEK 1: WHAT ARE THE CHANCES OF HAPPENING? Flashcards

1
Q

Describe the process of hypothesis testing.

A

*Pose a question
*Create a hypothesis
*Make predictions
*Design & carry out experiments to test your predictions
*Analyse your data using statistics

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2
Q

What is null hypothesis?

A

-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.

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3
Q

What is Research or Alternative hypothesis?

A

States that there is a difference between groups
Can accept or reject it

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4
Q

What is a probability value?
What is the standard p-value?

A

*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

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5
Q

What if P value is more than 0.05?

A

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.

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6
Q

what if P value is less than 0.05?

A

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.

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7
Q

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.

A
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8
Q

How does sample size affect the interpretation of p-values?

A

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.

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9
Q

What is type I error?

A

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.

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10
Q

What is type II error?

A

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.

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