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When assessing the influence of an intervention on particular parameters, it is important to consider the magnitude of the smallest worthwhile enhancement in the given parameter, and the uncertainty or noise in the test result (Hopkins 2004). Traditionally, inferential statistics are used to test the null-hypothesis, where a ‘P’ value is produced for an outcome statistic. The P value is the probability of obtaining any value larger than the observed effect, regardless of sign, (i.e. positive or negative) if the null hypothesis were true. When P<0.05, the null hypothesis is rejected and the outcome is said to be statistically significant (Fisher 1970). However, the P value does not provide information regarding the direction or size of the effect or, given sampling variability, the range of likely values (Batterham and Hopkins 2006). In fact, depending on sample size and variability, among other things, an outcome statistic with P<0.05 could represent an effect that is mechanistically, practically, or clinically irrelevant. On the other hand, a non-significant result of P>0.05 does not always imply the absence of a worthwhile effect. A combination of large measurement variability, and a small sample size may actually overshadow important effects (Batterham and Hopkins 2006).

When assessing elite athletes, the smallest worthwhile enhancement in a given parameter, such as performance, is often small (Hopkins 2004) and may be missed by traditional inferential statistics. Hopkins (2004) notes that for elite athletes competing in individual sports, the smallest worthwhile enhancement in performance would give the athlete an extra medal per 10 competitions. With this in mind, the required change in performance is 0.3 of the typical variation in an athlete's performance from competition to competition, equating to ~0.3-1% when expressed as a change in power output, depending on the sport (Hopkins 2004). For team sport athletes, where a direct relationship between team and test performance is not present, an appropriate default for the smallest change in test performance is one-fifth 78

of the between-athlete standard deviation (a standardized or Cohen effect size of 0.20) (Hopkins 2004). An alternative approach to P values is the use of confidence intervals and magnitude based inferences.

Confidence intervals represent the likely range of the true, real, or population value of the statistic (Batterham and Hopkins 2006). They can be used to interpret the magnitude of the size of the true value, and also the direction of the statistic (positive or negative) (Batterham and Hopkins 2006). Such values also highlight non-significant outcomes, where the confidence intervals cross the boundaries of negative and positive effects (Figure 2.2) (Batterham and Hopkins 2006). To further characterise the magnitude or size of the effect, the use of effect size statistics can be applied. Hopkins suggests categorising effect size statistics according to the following: <0.2 trivial, 0.2-0.6 small, 0.6-1.2 moderate, 1.2-2.0 large, 2.0-4.0 very large, >4.0 extremely large (Hopkins 2004).

Figure 2.6: Negative, positive and non-significant magnitudes. Only 3 inferences can be drawn when the possible magnitudes represented by the likely range in the true value of an outcome statistic (the confidence interval, shown by horizontal bars) are determined by referring to a 2- level (positive and negative) scale of magnitudes. Source Batterham et al 2006, pg 52.

2.9.2 The countermovement jump

The CMJ is a practical tool routinely (Cormack, Newton et al. 2008a; Cormack, Newton et al. 2008b) used to quantify neuromuscular fatigue and the extent of recovery in athletes (Carlock, 79

Smith et al. 2004; Cormack, Newton et al. 2008c). The CMJ is far less time consuming and demanding of the athlete compared to other performance measures such as sprint testing (single and repeated) and dynamometry. It is essential to quantify the impact of training/competition on the neuromuscular system to allow effective planning of training (Cormack, Newton et al. 2008a), to monitor recovery progress, and the effectiveness of recovery. The elastic behaviour of leg extensor muscles are similar in a vertical jump and running (Bosco, Montanari et al. 1987). Hence, when running is a principal component of a sport, a vertical jump assessment, such as the CMJ, is useful for assessing neuromuscular fatigue (Bosco, Montanari et al. 1987), and displays a high level of reliability (Table 2.3). The CMJ has been used extensively and validated as an indicator of neuromuscular fatigue amongst AF athletes (Cormack, Newton et al. 2008a; Cormack, Newton et al. 2008c). Although this does not reflect the athlete’s ability to replicate in-game physical performance, it can be used as a monitoring tool. When the athlete’s typical ‘free from fatigue’ jump performance is determined, as well as their natural variation in said performance, substantial deviations in performance may indicate fatigue. The CMJ is also used to evaluate changes in lower limb force and power capabilities following intensive training programs (Sheppard, Cormack et al. 2008), a predictor of strength and weightlifting performance (Carlock, Smith et al. 2004; Nuzzo, McBride et al. 2008; Vizcaya, Viana et al. 2009), and a training adaptation monitoring tool (Cormie, McBride et al. 2009).

Table 2.3: Reliability of measures obtained using a countermovement jump. Population Measurement tool Measurement variable CV % (TE) ICC Elite AF athletes (Cormack, Newton et al. 2008c)

Force platform Mean force (N) Relative mean force (N/kg) Flight time (sec)

1.1 % (13) 1.2 % (13) 2.9 % (0.017) - - - Students and colleagues (Slinde, Suber et al. 2008)

Contact mat Calculated jump height - 0.93 National level weight lifters (Carlock, Smith et al. 2004)

Contact mat Calculated jump height

- 0.98

Physically active men (Moir, Button et al. 2004)

Contact mat Calculated jump height 2.4 % (95 % CI 1.5-3.9) 0.93, 95 % CI 0.85- 0.98 Physical education students (Markovic, Dizdar et al. 2004)

Contact mat Calculated jump height 2.8 % 0.98 Experienced jumpers (Brandenburg, Pitney et al. 2007)

Contact mat Calculated jump height

- 0.97

Physically active men (Hori, Newton et al. 2009)

Force platform Peak power, peak force, and peak velocity

1.3-4.1 % 0.92-0.98

Reliability reported as Coefficient of variation % (CV%); technical error (TE). The test-retest reliability is also shown with the intra class correlation coefficient (ICC).