# 30 Tests for the mean difference (paired data)

So far, you have learnt to ask a RQ, identify different ways of obtaining data, design the study, collect the data describe the data, summarise data graphically and numerically, understand the tools of inference, and to form confidence intervals.

In this chapter, you will learn about hypothesis tests for the mean difference (i.e., for paired data). You will learn to:

• conduct hypothesis tests for the mean difference with paired data
• determine whether the conditions for using these methods apply in a given situation. ## 30.1 Introduction: Insulation The Electricity Council in Bristol wanted to determine if a certain type of wall-cavity insulation reduced energy consumption in winter, on average.473 Their RQ was:

Is there a mean saving in energy consumption due to adding insulation?

The data collected are shown in the table below. These data were used in Sect. 23, where a CI was constructed for the mean energy saving.

For these data, finding the difference in energy consumption for each house seems sensible. The data are paired (Sect. 23.1): the same unit of analysis is measured twice on the same variable (before and after), and the mean change is of interest.

Pairing the values for each house makes sense; hence finding the difference in energy consumption at each house makes sense.

The parameter is $$\mu_d$$, the population mean saving in energy consumption.

Making clear how the differences are computed is important.

Here, the differences could be computed as the Before minus After (the energy consumption saving), or the After minus Before (the energy consumption increase).

Either is fine, as long as you are consistent throughout. The meaning of any conclusions will be the same.

In this case, discussing energy savings seems most natural, so we compute the differences as Before minus After.

## 30.2 Hypotheses and notation: Mean differences

The RQ asks if the mean energy saving in the population is zero or not. The parameter is the population mean difference. To make things clear, notation is needed (recapping Sect. 23.3):

• $$\mu_d$$: The population mean difference.
• $$\bar{d}$$: The sample mean difference.
• $$s_d$$: The sample standard deviation of the differences.
• $$n$$: The number of differences.
• $$\text{s.e.}(\bar{d})$$: The standard error of the mean differences, where $$\displaystyle \text{s.e.}(\bar{d}) = \frac{s_d}{\sqrt{n}}$$.

The hypotheses, therefore, can be written in terms of the parameter $$\mu_d$$. The null hypothesis is 'there is no change in the energy consumption, in the population':

• $$H_0$$: $$\mu_d=0$$.

As noted in Sect. 29.2, the null hypothesis states that there is 'no difference, no change, no relationship', as measured by a parameter value. This hypothesis, the initial assumption, postulates that the mean difference may not be zero in the sample due to sampling variation.

Since the RQ asks specifically if the insulation saves energy, the alternative hypothesis will be a one-tailed hypothesis:

• $$H_1$$: $$\mu_d>0$$ (one-tailed).

This hypothesis says that the mean energy saving in the population is greater than zero. The alternative hypothesis is one-tailed because of the wording of the RQ. Recall that the differences are defined as energy savings.

## 30.3 Sampling distribution: Mean differences

Initially, assume that $$\mu_d = 0$$. However, the sample mean energy saving will vary depending on which sample is randomly obtained, even if the mean saving in the population is zero: the sample mean energy saving has sampling variation and hence a standard error. The sampling distribution of $$\bar{d}$$ can be described, which describes what values of the statistic might be expected in the sample if the $$\mu_d=0$$.

Answering the RQ requires data. The data should be summarised numerically (using your calculator or software, such as jamovi (Fig. 30.1) or SPSS (Fig. 30.2)), and graphically (Fig. 23.1). FIGURE 30.1: jamovi output for the insulation data FIGURE 30.2: SPSS output for the insulation data

The sample mean difference is $$\bar{d}=0.5400$$... but this value of $$\bar{d}$$ will vary from sample to sample even if $$\mu_d=0$$. The amount of variation in $$\bar{d}$$ is quantified using the standard error. More precisely, the possible values of the sample mean differences can, under certain conditions, described using

• an approximate normal distribution; with
• a mean of $$\mu_d = 0$$ (taken from $$H_0$$); and
• a standard deviation (called the standard error) of $$\text{s.e.}(\bar{d}) = s_d/\sqrt{n} = 0.3212$$, where $$s_d$$ is the standard deviation of the differences.

This describes what can be expected from the possible values of $$\bar{d}$$ (Fig. 30.3), just through sampling variation (chance) if $$\mu_d=0$$. FIGURE 30.3: The sampling distribution of sample means, if the energy saving in the population really was zero

## 30.4 The test statistic: Mean differences

The sample mean difference can be located on the sampling distribution (Fig. 30.3) by computing the $$t$$-score:

$t = \frac{0.54 - 0}{0.3212} = 1.681,$ following the ideas in Equation (28.1).

Software computes the $$t$$-score too (jamovi: Fig. 30.4; SPSS: Fig. 30.5). The $$t$$-score places the observed sample statistic on the sampling distribution.

## 30.5$$P$$-values: Mean differences

A $$P$$-value determines if the sample data are consistent with the assumption (Table 29.1).

Since $$t=1.681$$, the one-tailed $$P$$-value is between 2.5% and 16% based on the 68--95--99.7 rule. This is a wide, and inconclusive, interval.

Software gives a more precise $$P$$-value (jamovi: Fig. 30.4; SPSS: Fig. 30.5): the two-tailed $$P$$-value is $$0.127$$, so the one-tailed $$P$$-value is $$0.127/2 = 0.0635$$. FIGURE 30.4: jamovi output for the insulation data FIGURE 30.5: SPSS output for the insulation data

The software clarifies how the differences have been computed:

• jamovi: At the left of the output (Fig. 30.4), the order implies the differences are found as Before minus After.

• SPSS: At the left of the output (Fig. 30.5), the difference is described as Before $${}-{}$$ After.

Click on the hotspots in the following image, to see how to read the SPSS output.

## 30.6 Conclusions: Mean differences

The one-tailed $$P$$-value is $$0.0635$$, suggesting only slight evidence supporting $$H_1$$. To write a conclusion, an answer to the RQ is needed, plus evidence leading to that conclusion; and some summary statistics, including a CI (indicating the precision of the statistic):

Slight evidence exists in the sample (paired $$t=1.68$$; one-tailed $$P=0.0635$$) of a mean energy saving in the population (mean saving: 0.54 MWh; $$n = 10$$; 95% CI from $$-0.19$$ to $$1.27$$ MWh) after adding the insulation.

The wording implies the direction of the differences (by talking of 'savings').

Of course, statistically validity should be checked; this was done in Sect. 23.9, but the validity conditions are given again in the next section, for completeness.

Example 30.1 (COVID lockdown) A study of $$n = 213$$ Spanish health students474 measured (among other things) the number of minutes of vigorous physical activity (PA) performed by students before and during the COVID-19 lockdown (from March to April 2020 in Spain).

These numerical summary of the data are shown in Example 23.1, so we do not repeat it here. We define the differences as the number of minutes of vigorous PA before the COVID lockdown, minus the number of minutes of vigorous PA during the COVID lockdown. A difference is computed for each participant, so the data are paired.

Using this definition, a positive difference means the Before value is higher; hence, the differences tell us how much longer the student spent doing vigorous PA before the COVID lockdown. Similarly, a negative value means that the During value is higher.

The RQ is

For Spanish health students, is there a mean change in the amount of vigorous PA during and before the COVID lockdown?

In this situation, the parameter of interest is the population mean difference $$\mu_d$$, the mean amount that students spent in vigorous PA before the lockdown compared to during the lockdown. The hypotheses are:

• $$H_0$$: $$\mu_d = 0$$
• $$H_1$$: $$\mu_d \ne 0$$ (i.e., two-tailed)

The mean difference is $$\bar{d} = -2.68$$ minutes, with a standard deviation of $$s_d = 51.30$$ minutes. However, we know that the sample mean difference could vary from sample to sample, so has a standard error: $\text{s.e.}(\bar{d}) = \frac{s_d}{n} = \frac{51.30}{\sqrt{213}} = 3.515018.$

The test statistic is

$t = \frac{\bar{d} - \mu_d}{\text{s.e.}(\bar{d})} = \frac{-2.68 - 0}{3.515018} = -0.76.$

This is a very small value, so (using the 68--95--99.7 rule) the $$P$$-value will be very large: a sample mean difference of -2.68 minutes could easily have happened by chance even if the population mean difference was zero.

We write:

There is no evidence (paired $$t = -0.76$$, $$P > 0.10$$) of a mean change in the amount of vigorous PA before and during lockdown (sample mean 2.68 minutes greater during lockdown; standard deviation: 51.30 minutes).

## 30.7 Statistical validity conditions: Mean differences

As with any inferential procedure, these results apply under certain conditions. For a hypothesis test for the mean of paired data, these conditions are the same as for the CI for the mean difference for paired data (Sect. 23.9), and similar to those for one sample mean.

The test above is statistically valid if one of these conditions is true:

1. The sample size of differences is at least 25; or
2. The sample size of differences is smaller than 25, and the population of differences has an approximate normal distribution.

The sample size of 25 is a rough figure here, and some books give other values (such as 30). This condition ensures that the distribution of the sample means has an approximate normal distribution so that we can use the 68--95--99.7 rule.

Provided the sample size is larger than about 25, this will be approximately true even if the distribution of the individuals in the population does not have a normal distribution. That is, when $$n>25$$ the sample means generally have an approximate normal distribution, even if the data themselves don't have a normal distribution.

In addition to the statistical validity condition, the test will be

Example 30.2 (Statistical validity) For the insulation data used above, the sample size is small, so the test will be statistically valid if the differences in the population follow a normal distribution.

We don't know if they do, though the sample data (Fig. 23.1) don't identify any obvious doubts. So the test is possibly statistically valid, but we can't be sure.

Example 30.3 (COVID lockdown) In Example 30.1 concerning COVID lockdowns, the sample size was 213 Spanish health students.

Since the sample size is much larger than 25, the test is statistically valid.

## 30.8 Example: Endangered species

A study of endangered species475 examined

...whether perceived physical attractiveness of a species impacted participants' attitudes toward supporting and protecting the species...

--- p. 1703

To do so, 210 undergraduate students were surveyed about 14 animals on various aspects of supporting and protecting them. Part of the data are summarised below, for two animals when asked about 'support to protect the animal from illicit trade'. Larger values means greater support for protecting the animal from illicit trade.

Species Mean score Standard deviation
Bay Checkerspot Butterfly 3.10 1.06
Valley Elderberry Longhorn Beetle 2.33 1.13
Difference 0.77 1.07

(Notice that the standard deviation of the difference is not the difference between the two given values of the standard deviation.)

The difference is defined as each student's score for the butterfly (deemed more attractive) minus their score for the beetle (deemed less attractive). A positive value therefore means more support (on average) for the butterfly.

The RQ is whether there is a mean difference between support for each animal, so the parameter is $$\mu_d$$, the population mean difference.

The researchers wished to test if

...animals perceived as more physically attractive (i.e., the butterfly) compared to those which are perceived as less physically attractive (i.e., the beetle) will receive relatively more support to prevent the species from illicit trade

--- p. 1704

Given how the difference are defined, the hypotheses are:

• $$H_0$$: $$\mu_d = 0$$
• $$H_1$$: $$\mu_d > 0$$ (i.e., one-tailed, based on the researchers' purpose)

The mean difference is $$\bar{d} = 0.77$$ and $$s_d = 1.07$$. The value of $$\bar{d}$$ will vary from sample to sample, so has a standard error:

$\text{s.e.}(\bar{d}) = \frac{s_d}{\sqrt{n}} = \frac{1.07}{\sqrt{210}} = 0.073837.$

The value of the test statistic is

$t = \frac{\bar{d} - \mu_d}{\text{s.e.}(\bar{d})} = \frac{0.77 - 0}{0.073837} = 10.43,$ which is a very large value. Hence, the $$P$$-value will be very small, certainly less than $$0.05$$.

Since the sample size is much larger than 25, the test will be statistically valid.

We write:

There is very strong evidence ($$t = 10.43$$; one-tailed $$P<0.001$$) that the mean difference in support for protecting the Bay Checkerspot Butterfly from illicit trade is greater than support for protecting the Valley Elderberry Longhorn Beetle from illict trade (mean difference: 0.77; standard deviation: 1.07; 95% CI for the difference: 0.62 to 0.92).

## 30.9 Example: Blood pressure A US study478 was conducted to determine how CHD risk factors were assessed among parts of the population. Subjects were required to report to the clinic on multiple occasions.

One RQ of interest is:

Is there a mean difference in blood pressure measurements between the first and second visits?

The parameter is $$\mu_d$$, the population mean reduction in blood pressure.

Each person has a pair of diastolic blood pressure (DBP) measurements: One each from their first and second visits. The data shown below, are from 141 people. These data were shown in Sect. 23.10. The differences could be computed in one of two ways:

• The observation from the first visit, minus the observation from the second visit: the reduction in BP; or
• The observation from the second visit, minus the observation from the first visit: the increase in BP.

Either way is fine, as long as the order remains consistent, and the direction is made clear. Here, the observations from the first visit minus the observation from the second visit will be used, so that the differences represent the decrease in BP from the first to second measurement.

The appropriate graphical summary is a histogram of differences (Fig. 23.4); the numerical summary is shown in Table 30.1. Notice that having the information about the differences is essential, as the RQ is about the differences.

TABLE 30.1: The numerical summary for the diabetes data (in mm Hg). The differences are the second visit value minus the first visit value: the decreases in diastolic blood pressure from the first to second visit
Mean Standard deviation Standard error Sample size
DBP: First visit 94.48 11.473 0.966 141
DBP: Second visit 92.52 11.555 0.973 141
Decrease in DBP 1.95 8.026 0.676 141

As always (Sect. 29.2), the null hypothesis is the 'no difference, no change, no relationship' position, proposing that the mean difference in the population is non-zero due to sampling variation:

• $$H_0$$: $$\mu_d=0$$ (differences: $$\text{first} - \text{second}$$);
• $$H_1$$: $$\mu_d \ne 0$$.

The alternative hypothesis is two-tailed because of the wording of the RQ. As usual, assume that $$H_0$$ is true, and then the evidence is evaluated to determine if it contradicts this assertion.

The sampling distribution describes how the sample mean difference is expected to vary from sample to sample due to sampling variation, when $$\mu_d=0$$. Under certain circumstances, the sample mean differences are likely to vary with a normal distribution, with a mean of 0 (from $$H_0$$) and a standard deviation of $$\text{s.e.}(\bar{d}) = 0.676$$.

The relative value of the observed sample statistic is found by computing a $$t$$-score, using software (jamovi: Fig. 30.6; SPSS: Fig. 30.7), or manually (Eq. (28.1), using the information in Table 30.1):

\begin{align*} t &= \frac{\text{sample statistic} - \text{assumed population parameter}} {\text{standard error of the statistic}}\\ &= \frac{1.950 - 0}{0.676} = 2.885. \end{align*} Either way, the $$t$$-score is the same. FIGURE 30.6: jamovi output for the diabetes data FIGURE 30.7: SPSS output for the diabetes data

A $$P$$-value is then needed to decide if the sample is consistent with the assumption. Using the 68--95--99.7 rule, the approximate two-tailed $$P$$-value is much smaller than 0.05. Alternatively, the software output (Fig. 30.6; Fig. 30.7) reports the two-tailed $$P$$-value as $$P=0.005$$.

We conclude:

Strong evidence exists in the sample (paired $$t = 2.855$$; two-tailed $$P = 0.005$$) of a population mean difference between the first and second DBP readings (mean difference $$1.95$$ mm Hg higher for first reading; 95% CI from $$0.61$$ to $$3.3$$ mm Hg; $$n = 141$$).

Since $$n>25$$, the results are statistically valid.

Just saying 'there is evidence of a difference' is insufficient.

You must state which measurement is, on average, higher (that is, what the differences mean).

## 30.10 Summary

Consider testing a hypothesis about a population mean difference $$\mu_d$$, based on the value of the sample mean difference $$\bar{d}$$. Under certain statistical validity conditions, the sample mean difference varies with an approximate normal distribution centered around the hypothesised value of $$\mu_d$$, with a standard deviation of

$\text{s.e.}(\bar{d}) = \frac{s_d}{\sqrt{n}}.$ This distribution describes what values of the sample mean difference could be expected if the value of $$\mu_d$$ in the null hypothesis was true. The test statistic is

$t = \frac{ \bar{d} - \mu_d}{\text{s.e.}(\bar{d})},$ where $$\mu_d$$ is the hypothesised value in the null hypothesis. The $$t$$-score describes what value of $$\bar{d}$$ was observed in the sample, relative to what was expected. The $$t$$-value is like a $$z$$-score, so an approximate $$P$$-value can be estimated using the 68--95--99.7 rule, or is found using software. The $$P$$-values helps determine if the sample evidence is consistent with the assumption, or contradicts the assumption.

The following short video may help explain some of these concepts:

## 30.11 Quick review questions

A study479 compared joint pain in stroke patients before and after a supervised exercise treatment. Participants ($$n=34$$) were assessed before and after treatment.

The mean change in joint pain after 13 weeks was 1.27 (with a standard error of 0.57) using a standardised tool.

1. True or false? Only 'before and after' studies can be paired.

2. True or false? The null hypothesis is about the population mean difference.

3. The value of the test statistic (to two decimal places) is

4. The two-tailed $$P$$-value will be

Progress:

## 30.12 Exercises

Selected answers are available in Sect. D.28.

Exercise 30.1 People often struggle to eat the recommended intake of vegetables. In one study exploring ways to increase vegetable intake in teens,480 teens rated the taste of raw broccoli, and raw broccoli served with a specially-made dip. (These data were also seen in Exercise 23.1.)

Each teen ($$n = 101$$) had a pair of measurements: the taste rating of the broccoli with and without dip. Taste was assessed using a '100 mm visual analog scale', where a higher score means a better taste. In summary:

• For raw broccoli, the mean taste rating was $$56.0$$ (with a standard deviation of $$26.6$$);
• For raw broccoli served with dip, the mean taste rating was $$61.2$$ (with a standard deviation of $$28.7$$).

Because the data are paired, the differences are the best way to describe the data. The mean difference in the ratings was $$5.2$$, with standard error of $$3.06$$.

Perform a hypothesis test to see if the use of dip increases the mean taste rating.

Exercise 30.2 In a study of hypertension,481 patients were given a drug (Captopril) and their systolic blood pressure measured immediately before and two hours after being given the drug (data shown).

The aim is to see if there is evidence of a reduction in blood pressure after taking Captopril. (This study was also seen in Exercise 23.2.)

Using these data and the software output (jamovi: Fig. 30.8; SPSS: Fig. 30.9):

1. Explain why it is probably more sensible to compute differences as the Before minus the After measurements. What do the differences mean when computed this way?
2. Compute the differences.
3. Construct a suitable graph for the differences.
4. Write down the hypotheses.
5. Write down the $$t$$-score.
6. Write down the $$P$$-value.
7. Write a conclusion.
TABLE 30.2: The Captopril data: before after after systolic blood pressures (in mm Hg)
Before After Before After
210 201 173 147
169 165 146 136
187 166 174 151
160 157 201 168
167 147 198 179
176 145 148 129
185 168 154 131
206 180 FIGURE 30.8: jamovi output for the Captoril data FIGURE 30.9: SPSS output for the Captoril data

Exercise 30.3 A study482 examined the effect of exercise on smoking. Men and women were assessed on a range of measures, including the 'intention to smoke'.

(This study was also seen in Exercise 23.3.)

'Intention to smoke', and other measures, were assessed both before and after exercise for each subject, using the 10-item quantitative Questionnaire of Smoking Urges -- Brief scale,483 and the quantitative Minnesota Nicotine Withdrawal Scale.484

Smokers (defined as people smoking at least five cigarettes per day) aged 18 to 40 were enrolled for the study. For the 23 women in the study, the mean intention to smoke after exercise reduced by 0.66 (with a standard error of 0.37).

Perform a hypothesis test to determine if there is evidence of a population mean reduction in intention to smoke for women after exercising.

Exercise 30.4 In a study485 conducted at the Adelaide Children's Hospital:

...a group of beta thalassemia patients [...] were treated by a continuous infusion of desferrioxamine, in order to reduce their ferritin content...

Using the data (shown below), conduct a hypothesis test to determine if there is evidence that the treatment reduces the ferritin content, as intended.

Exercise 30.5 In an attempt to reduce vehicle speeds on freeway exit ramps, a Chinese study tried using additional signage.487

At one site studied (Ningxuan Freeway), speeds were recorded at various points on the freeway exit for vehicles before the extra signage was added, and then after the extra signage was added.

From this data, the deceleration of each vehicle was determined (data below) as the vehicle left the 120 km/h speed zone and approached the 80 km/hr speed zone.

Use the data, and the summary in Table 30.3, to test the RQ:

At this freeway exit, is the mean vehicle deceleration the same before extra signage is added and after extra signage is added?

TABLE 30.3: The signage data summary (in m/s-squared)
Mean Std deviation Sample size Std error
Before 0.0745 0.0494 0.00802 38
After 0.0765 0.0521 0.00814 41
Change -0.0020 0.00181