## 1 Introduction  
In order to determine the fatigue life or fatigue strength of a structural component, we use, depending on the assumed loading condition, an appropriate computational model. Using any computational model, we need to know the fatigue characteristics of the given element. This applies, for example, to the design of machine components such as cutting drum shafts [1], bicycle frames [2], railway axles [3], or bridge crane shafts [4]. Recommendations such as ISO [5] or ASTM [6] standards can be used to determine such characteristics. In the high-cycle range, we most often use a linear regression describing the Basquin equation [7], which describes the relationship of the stress amplitude to the number of cycles (Stress--Number of cycles; \(S\)--\(N\)) and is written as follows:  
\[\log(N)=m\,\log(S_{a})+c, \tag{1}\]  
where \(S_{a}\) is the stress amplitude, \(N\) is the number of cycles, \(m\) is the slope coefficient and \(c\) is the intercept.  
A scheme of the procedure for determining fatigue characteristics in the high-cycle range is shown in Figure 1. Since fatigue tests are costly and expensive, efforts are being made to keep them to a minimum. For example, the testing time for one sample (not including sample preparation, mounting, setting test parameters in the testing machine, etc.) for a frequency of 30 Hz and a fatigue life of \(10^{5}\) cycles is less than an hour. However, for \(10^{6}\) cycles, it is more than 9 h. On the other hand, the cost of conducting the test per sample was calculated by [PERSON] [8] at USD 500 to USD 1000. Many researchers have made an effort to determine the minimum number of samples. [PERSON] [9] determined that at least 10 samples are required for a 10% probability of failure and a 90% confidence level. In contrast, [PERSON] and [PERSON] in their paper [10] propose seven samples for a 5% probability of failure and a 95% confidence level for each load level. A different approach was presented by [PERSON] et al. in their paper [11], where they identified seven samples with a 50% probability of failure.  
Various statistical methods have been developed to improve the accuracy of fatigue characteristic estimation. One of them is the backward statistical inference method presented by [PERSON] et al. [12]. This method assumes that the probability distribution for each load level is the same. An improvement of the backward statistical inference method was presented by [PERSON] et al. in a paper [13]. Both methods require at least 15 samples. Another approach was presented by [PERSON] and [PERSON] in a paper [14], where non-invasive polynomial chaos was used to determine fatigue characteristics. This approach requires 16 samples. Meanwhile, in paper [15], [PERSON] et al. proposed an \(\alpha\)-S-N method based on uncertainty theory. This method obtained better results than the least-squares method. Unfortunately, it still requires 15 samples. It should be emphasized that the statistical methods presented above are intended to improve the accuracy of estimation from experimental data, but indicate that they cannot replace standard testing. This is related to many factors affecting fatigue life, such as material properties, geometry of the test object, surface condition, environmental conditions, etc.  
This paper presents the guidelines contained in ISO 12107:2003 [16] for the number of samples for determining fatigue characteristics in the high-cycle range. The size of the experimental data set depends on the required accuracy of the estimated characteristics. The proposed normative values were compared with the classical statistical approach. In addition, the error was determined for characteristics with different data set counts.  
The purpose of this study is to determine the statistical power of the estimated fatigue characteristics in the high-cycle range with respect to the number of samples. The representation of the required number of samples for the determination of fatigue characteristics was referred to as normative recommendations.