BS1.8: Hypothesis Testing

OBJECTIVES
At the end of this section you should understand the concept of hypothesis testing.

Hypothesis Testing:

In the previous section you dealt with the first form of statistical inference, estimation. We selected a sample to draw conclusions about the population by calculating a confidence interval.

The second form of statistical inference is hypothesis testing. Hypothesis testing is a method used by researchers to determine how likely it is that observed differences between different sample estimates are entirely due to sampling error rather than underlying population differences.

Example:

Suppose a researcher wish to compare mean systolic blood pressure values between two groups (exposed and non-exposed). The following descriptive measures were computed:

Descriptive measures Sample #1
(Exposed)		 Sample #2
(Non-exposed)
Sample size 15 12
Mean systolic BP (mm Hg) 130 120
Standard deviation 22 20

We may hypothesise that the two means are similar. By means of hypothesis testing you can determine whether or not such statements are compatible with available data.

The procedure for testing the hypothesis is as follows:

  1. State the null hypothesis: Population means are equal (Mean1 - Mean2 = 0).

  2. Select a level of significance: P = 0.05

    The level of significance is a probability value, denoted by (P), that we use as a cut-off value by convention to reject the null hypothesis.

    We reject the null hypothesis when the P-value is less than or equal to 0.05 (P £ 0.05), implying that the two means (Mean1 and Mean2) are significantly different. On the contrary, when the P-value is greater than 0.05 (P > 0.05) then we cannot reject the null hypothesis.

    Also, there is a relationship between the level of significance (5%) and the level of confidence (95%), that is, 100% - 5% = level of confidence. So, if the P-value is less than 5% we are increasing level of confidence. A P-value is a measure of how much evidence we have against the null hypothesis. The smaller the P-value, the more evidence we have against the null hypothesis.

  3. Test of significance: Apply an appropriate statistical test. A test of significance is a procedure by which we verify an established hypothesis. It is used to decide whether the established null hypothesis is rejected or accepted.




Creative Commons License
General Introduction to Occupational Health: Occupational Hygiene, Epidemiology & Biostatistics by Prof Jonny Myers is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 South Africa License
.