Next, we compute the probability of seeing the effect (E) assuming that the null hypotheses is true.
i.e. P(E | H₀)
This probability is referred to as the p-value.

If the p-value is small, we conclude that the effect is unlikely to have occurred by chance, which suggests that the effect is real.

For more info:

final 1516.pdf

P.S. If you would like to check out my analysis, you have to download the attached file first :)

8.0b) Sampling Distribution of the Mean

8.0c) Difference Between Means

8.0d) Sampling Distribution of a Proportion

8.1a) Introduction to Hypothesis Testing

8.1b) Significance Testing

8.1c) Type I and II Errors

8.1d) One- and Two-Tailed Tests

8.1e) Interpreting Significant Results

8.1f) Interpreting Non-Significant Results

8.1g) Steps in Hypothesis Testing

8.1h) Significance Testing and Confidence Intervals

8.1i) Misconceptions about Significance Testing

8.2a) Testing a Single Mean

8.2b) Testing a Single Mean (Example)

8.3) Difference of sample means distribution (Example)

8.4) Large sample proportion hypothesis testing (Example)

8.5) Hypothesis test comparing population proportions (Example)

Trim 2, 2015/2016

Trim 2, 2014/2015

Z-Test Calculator for Single Sample

Z-Test Calculator for Two Sample Means

1-sample z-test for a population proportion

Z Score Calculator for 2 Population Proportions