統計分析技術 (Statistical Techniques) 的應用，在實證研究中扮演著相當重要的角色。在許多的實證研究中，研究人員針對研究問題的需要，從真實世界收集得到相關數據資料後，這些數據資料需要經過合適統計分析技術的使用與處理，以驗證研究假設是否得到支持。因此，統計分析技術的使用正確與否，關係著學術論文的品質。另一方面，由於個人電腦處理能力的快速發展，以及各類統計分析軟體的功能愈來愈強大，使得統計分析技術在研究上的應用更加普遍。由於統計分析技術在使用上的重要|生與相關軟體的易取得性，如何正確來使用這些統計分析技術，以求達到每個研究的實際需要，也就成為相當值得重視的課題。 本研究透過調查刊登在國內主要資訊管理期刊上的論文，來瞭解統計分析技術在國內資訊管理研究上的實際應用情形以及常見易犯錯誤。研究結果顯示：(1) 非實證研究目前在國內資管研究中仍然佔有較大的比重，但是實證研究有成長的趨勢。(2) 目前國內調查研究做的最多，約佔實證研究的一半。然而，實驗研究則有成長的趨勢。(3) 關於統計分析技術的使用，多變量分析技術的使用率高於簡單的敘述統計與推論統計技術，佔實證研究中的46%。並且其應用的高峰主要在最近五年。(4) 在所有的多變量分析技術中，以變異數分析使用次數最多。而且最近五年中，MANOVA的使用比例增加的相當快，ANOVA的使用比例則相對地降低。(5) 在應用多變量分析技術時，研究人員最常見的錯誤依次是：未進行或報導基本假設的檢定、末偵測與修正離群值、以及未符合樣本數最低限制。我們在最後也針對這些使用統計分析技術時的常見錯誤，提出對應的改善方法與建議。

In conducting an empirical study, researchers begin by defining the research questions, then collect data from the real world and used to apply appropriate statistical techniques to test the hypotheses, and finally draw conclusions from the results. Due to the increased computing power of personal computers and user friendliness of the statistical software, statistical techniques have become easy to use and widespread. Nevertheless, the proper and correct use of statistical techniques is critical to a rigorous study. This paper reviewed major information systems research (ISR) journal papers in Taiwan to determine (1) the distributions of various research methods used over time, (2) the distributions of various statistical techniques used over time, and (3) the frequencies of various mistakes found in applying the statistical techniques. Results indicated that: first, non-empirical studies have dominated the ISR in the past twelve years. However, the percentage of empirical studies is growing. Second, survey research is the most popular research method. In addition, the percentage of studies which employ laboratory experiments is rapidly growing. Third, multivariate analysis techniques are the most frequently used statistical techniques. This is followed by the statistical inference techniques. Fourth, ANOVA (analysis of variance) is the most frequently used multivariate analysis technique. However, the percentage of MANOVA (multivariate analysis of variance) has risen rapidly in the last five years. Finally, when using multivariate analysis techniques, commonly found mistakes include an inability to test basic assumptions behind the multivariate analysis techniques, being unable to detect and correct outliers, and sample sizes which are too small. The implications of these findings and ways of improving the rigorousness of the study are also provided.