【英語論文の書き方】第55回 実験計画について

2018年4月26日 10時00分

(1) Using hyphens in compound adjectives
(2) Spaces between sentences and in mathematical expressions
(3) Checking the “Instructions to authors” of the relevant journal before you start preparing your paper


Designing effective research By Geoffrey Hart

One of the biggest problems I encounter in the journal manuscripts that I edit relates to the experimental design. Often, authors obtain results that are not statistically significant, even though it seems likely that the differences that they observed between treatments were large and significant. When we study biological systems, there is always a risk that high variability will make it difficult to detect significant differences. But there are proven techniques that can reduce the risk. In this article, I’ll remind you of some of the most important ones.

Control experimental conditions

One problem with field research is that so many factors can affect the outcomes, particularly for living organisms. This is why many researchers prefer laboratory research, because they control the variables. If it’s necessary to limit variability in the field, the first step is to review the literature to identify the factors that can affect the outcomes.
Understanding these factors lets you choose sites that have similar values of these factors to minimize their impact. It also lets you perform stratified sampling, in which each group of similar properties forms a “stratum”, with reduced variation within the group.

Sample sizes

Field research can be hard; when I did plant physiology research many years ago, the measurements took long enough that by the time I reached the final plant, it was time to remeasure the first plant. Time and budget constraints encourage many researchers to use a very small sample size; though this makes better use of limited time and money, it often results in a lack of significant differences. Assuming there is no inherent problem that leads to high variation (e.g., a skewed probability distribution that results from some physical process), increasing the sample size will usually help.
One way to increase the likelihood of significance is to examine sample sizes in the literature for studies similar to yours. Studies where the results appeared different, but the difference was not significant, suggest that you should use a larger sample size.
If you prefer a more mathematical approach, you can predict the required sample size using the reported variances (s2). Wikipedia provides a good overview of this subject
You can also start by determining the “statistical power” (the probability that you will correctly reject the null hypothesis) of your design.


In surveying “triangulation” means examining the position of a point by examining it from two or more angles and using basic geometry to calculate the position—something that would not be easy from only one position. In research, triangulation means confirming a finding from multiple directions.
For example, to measure gene expression, you can either quantify the protein products of the gene or the quantity of its RNA transcripts. In ecology, you can measure the volume of carbon sequestered by vegetation using either species-specific allometric equations or by random destructive sampling. In both cases, the two results will be correlated, but different. If one test fails to produce a significant result, the other test may provide the desired result.
Even if it doesn’t, identifying the same trend in both tests makes it more likely that a difference exists.

Confirming you can analyze your data

Sometimes an experiment seems to be well designed, but it becomes difficult to perform statistical significance tests for the data it produces. A simple example might be designing an experiment based on the assumption that the data will be normally distributed, when in fact the data follows a highly skewed or non-normal distribution or even a multimodal distribution such as binomial. The literature will often reveal this, so you can ask a statistician for advice on how to handle such data in your experimental design. You can sometimes still find a way to analyze the data, but it’s better to start with appropriate data in the first place. With a little knowledge of the likely variance structure of the phenomenon or organism that you’re studying, and the likely probability distribution of the results, they can help you find an experimental design that maximizes the ability of your study to detect significant results.


Most students learn these concepts early in their university career, but a surprising number forget them when it comes time to design their own research. I hope that this article will help you avoid this mistake.





第1回 if、in case、when の正しい使い分け:確実性の程度を英語で正しく表現する

第2回 「装置」に対する英語表現

第3回 助動詞のニュアンスを正しく理解する:「~することが出来た」「~することが出来なかった」の表現

第4回 「~を用いて」の表現:by と with の違い

第5回 技術英文で使われる代名詞のitおよび指示代名詞thisとthatの違いとそれらの使用法

第6回 原因・結果を表す動詞の正しい使い方:その1 原因→結果

第7回 原因・結果を表す動詞の使い方:その2 結果→原因

第8回 受動態の多用と誤用に注意

第9回 top-heavyな英文を避ける

第10回 名詞の修飾語を前から修飾する場合の表現法

第11回 受動態による効果的表現

第12回 同格を表す接続詞thatの使い方

第13回 「技術」を表す英語表現

第14回 「特別に」を表す英語表現

第15回 所有を示すアポストロフィー + s ( ’s) の使い方

第16回 「つまり」「言い換えれば」を表す表現

第17回 寸法や重量を表す表現

第18回 前置詞 of の使い方: Part 1

第19回 前置詞 of の使い方: Part 2

第20回 物体や物質を表す英語表現

第21回 句動詞表現より1語動詞での表現へ

第22回 不定詞と動名詞: Part 1

第23回 不定詞と動名詞の使い分け: Part 2

第24回 理由を表す表現

第25回 総称表現 (a, theの使い方を含む)


第27回 「0~1の数値は単数か複数か?」

第28回 「時制-現在形の動詞の使い方」

第29回  then, however, therefore, for example など接続副詞の使い方​

第30回  まちがえやすいusing, based onの使い方-分詞構文​

第31回  比率や割合の表現(ratio, rate, proportion, percent, percentage)

第32回 英語論文の書き方 総集編

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第35回 Quality Review Issue No. 25 略語を書き出すときによくある間違いとは?​

第36回 Quality Review Issue No. 26 %と℃の前にスペースを入れるかどうか

第37回 Quality Review Issue No. 27 同じ種類の名詞が続くとき冠詞は付けるべき?!​

第38回 Quality Review Issue No. 22  日本人が特に間違えやすい副詞の使い方​

第39回 Quality Review Issue No. 21  previous, preceding, earlierなどの表現のちがい

第40回 Quality Review Issue No. 20 using XX, by XXの表現の違い

第41回 Quality Review Issue No. 19 increase, rise, surgeなど動詞の選び方

第42回 Quality Review Issue No. 18 論文での受動態の使い方​

第43回 Quality Review Issue No. 17  Compared with とCompared toの違いは?​

第44回 Reported about, Approach toの前置詞は必要か?​

第45回 Think, propose, suggest, consider, believeの使い分け​

第46回 Quality Review Issue No. 14  Problematic prepositions scientific writing: by, through, and with -3つの前置詞について​

第47回 Quality Review Issue No. 13 名詞を前から修飾する場合と後ろから修飾する場合​

第48回 Quality Review Issue No. 13 単数用法のThey​

第49回 Quality Review Issue No. 12  study, investigation, research の微妙なニュアンスのちがい

第50回 SinceとBecause 用法に違いはあるのか?

第51回 Figure 1とFig.1の使い分け

第52回 数式を含む場合は現在形か?過去形か?

第53回 Quality Review Issue No. 8  By 2020とup to 2020の違い

第53回 Quality Review Issue No. 7  high-accuracy data? それとも High accurate data? 複合形容詞でのハイフンの使用


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