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							<p>After researchers select the data/information to be used in their study, it must be <glossary placement="bottom" content="The careful study of research findings to look for trends, patterns or other evidence that will answer your research questions. It is important to note that in some fields an outcome of &ldquo;no findings&rdquo; is important and should be analysed and reported.">analysed</glossary>. Through analysis, you answer the question "Did I learn anything interesting and reportable?"</p>
							
							<h3>Best practices</h3>
							
							<p>The methods researchers use to analyse and interpret their findings are generally set by their area of research, being taught as part of specific training. You are responsible for knowing, understanding and using best practices in your field when you analyse and interpret your findings.</p>
							<p>As general rules, you should concentrate on the following areas when analysing your results:</p>
							
							<ol>
								<li><strong>Method:</strong> Use appropriate analytical methods, particularly if statistical analysis is required</li>
								<li><strong>Significance:</strong> Assess the significance of your findings</li>
								<li><strong>Alternatives:</strong> Consider other explanations.</li>
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							<p>How do researchers from different disciplines view data analysis? </p>
							
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							<instructional-text><p>Choose your discipline area from the menu below to watch interviews where researchers discuss their work and their views on various aspects of data analysis.</p></instructional-text>
							<instructional-text-accessible><p>In the following interviews, researchers discuss their work and provide their advice on how to conduct data analysis responsibly. You can choose the option that is tied most closely to your discipline.</p></instructional-text-accessible>
							
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									<action-link goto-panel="2"><h3>Biomedical Science</h3></action-link>
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									<accessible-text><h4>Arts & Humanities</h4></accessible-text>
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									<accessible-text><hr><h4>Biomedical Science</h4></accessible-text>
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									<accessible-text><hr><h4>Engineering</h4></accessible-text>
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									<accessible-text><hr><h4>Social Science</h4></accessible-text>
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								<h3>Arts & Humanities</h3>
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								<h3>Biomedical Science</h3>
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								<h3>Engineering</h3>
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								<h3>Natural Science</h3>
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								<h3>Social Science</h3>
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							<p>Researchers will not always agree on the best way to analyse information, but irrespective of the methods they use they should be honest and open about them so anyone looking at their work clearly understands what they have done.</p>
							
							<h3>Avoiding bias in analysis</h3>
							
							<p>Being biased is part of being human. But the key feature that differentiates research from other human activities is the intentional use of methodologies that try to reduce bias. Good researchers must always be aware of the possibility that they could either intentionally or unintentionally be using their analysis to confirm (subjective) rather than test (objective) their research hypotheses.</p>
							
							<instructional-text><p>Select the "tick" or the "cross" according to whether you think the statement on best practices in interpretation is correct or not. Read the feedback text to find out if you were correct, and use the "Next" button to move on to the next statement. </p></instructional-text>
							<instructional-text-accessible><p>Decide whether you think each statement on best practices in interpretation is correct or not. Continue on to find out if you were correct before moving on to the next statement.</p></instructional-text-accessible>
							
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							<checklist allow-restart="true" has-ticks-and-crosses="true">
								<check-item>
									<p>Choose the interpretive model that produces the most significant results. </p>
									<check-tick-cross>
										<check-tick incorrect><strong>Incorrect.</strong> In statistics, this practice is known as '<em>p</em>-hacking' (using multiple methods of analysis but only presenting the findings from the one that gave the lowest '<em>p</em>-value' (probability value), indicating a more significant result). In most cases, good research &ndash; both quantitative and qualitative &ndash; defines the method of analysis before data is collected.</check-tick>
										<check-cross correct><strong>Correct.</strong> In statistics, this practice is known as '<em>p</em>-hacking' (using multiple methods of analysis but only presenting the findings from the one that gave the lowest '<em>p</em>-value' (probability value), indicating a more significant result). In most cases, good research &ndash; both quantitative and qualitative &ndash; defines the method of analysis before data is collected.</check-cross>
									</check-tick-cross>
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									<p>If your results are somewhat statistically significant, extend your study and only stop once you have improved the significance.</p>
									<check-tick-cross>
										<check-tick incorrect><strong>Incorrect.</strong> As a rule, this is not a good practice. You should conduct your study as planned. If the results are not significant, you should go back to square one and revisit your study design to ensure you are not doing anything wrong. If your method is strong, an insignificant result is still a result, albeit one showing that your hypothesis was not correct. </check-tick>
										<check-cross correct><strong>Correct.</strong> As a rule, collecting more data to achieve statistical significance is not a good practice. Conduct your study as planned. If the results are not significant, then go back to square one and revisit your study design to ensure you are not doing anything wrong. If your method is strong, an insignificant result is still a result, albeit one showing that your hypothesis was not correct. </check-cross>
									</check-tick-cross>
								</check-item>
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									<p>Any generalisations you draw should be supported by your raw data.</p>
									<check-tick-cross>
										<check-tick correct><strong>Correct.</strong> Your raw data or the information you collected represents your findings. You should not ignore what you found. If there are legitimate reasons for excluding any of the raw data you collected, these should be explained. You should not base your findings on partial data without providing a clear justification. </check-tick>
										<check-cross incorrect><strong>Incorrect.</strong> Your raw data or the information you collected represents your findings. You should not ignore what you found. If there are legitimate reasons for excluding any of the raw data you collected, these should be explained. You should not base your findings on partial data without providing a clear justification. </check-cross>
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									<p>It is acceptable to drop, add or select information to increase the significance of a study.</p>
									<check-tick-cross>
										<check-tick incorrect><strong>Incorrect.</strong> It is unacceptable to drop, add or select information to increase the significance of a study without providing an explanation or clearly justifying your decision. Arbitrarily dropping and/or adding data could be considered either fabrication or falsification, and will get you into trouble.</check-tick>
										<check-cross correct><strong>Correct.</strong> It is unacceptable to drop, add or select information to increase the significance of a study without providing an explanation or clearly justifying your decision. Arbitrarily dropping and/or adding data could be considered either fabrication or falsification, and will get you into trouble. </check-cross>
									</check-tick-cross>
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									<p>If you are not sure what to conclude, present your personal beliefs and convictions.</p>
									<check-tick-cross>
										<check-tick incorrect><strong>Incorrect.</strong> Conclusions should come from the evidence, not personal beliefs. You can certainly present your personal beliefs, but you should be clear as to whether the evidence supports you so that readers can make up their own minds about what to conclude.</check-tick>
										<check-cross correct><strong>Correct.</strong> Conclusions should come from the evidence, not personal beliefs. You can certainly present your personal beliefs, but you should be clear as to whether the evidence supports you so that readers can make up their own minds about what to conclude.</check-cross>
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									<p>Limit the evaluation of your research findings to comparisons with similar research.</p>
									<check-tick-cross>
										<check-tick correct><strong>Correct.</strong> You should limit the evaluation of your research findings to comparisons with similar research. It can sometimes be very tempting to make broad comparisons with markedly different types of research. While this might make your work seem more exciting, it could very easily mislead others as to the importance or significance of your findings.</check-tick>
										<check-cross incorrect><strong>Incorrect.</strong> You should limit the evaluation of your research findings to comparisons with similar research. It can sometimes be very tempting to make broad comparisons with markedly different types of research. While this might make your work seem more exciting, it could very easily mislead others as to the importance or significance of your findings.</check-cross>
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									<p>Choose your control data/statistical method carefully so that it confirms your hypotheses.</p>
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										<check-tick incorrect><strong>Incorrect.</strong> Selecting control data or statistical methods based on your desired results is a clear example of bias.</check-tick>
										<check-cross correct><strong>Correct.</strong> Selecting control data or statistical methods based on your desired results is a clear example of bias.</check-cross>
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							<p>Using different analytical approaches is not, in and of itself, irresponsible. Indeed, researchers are expected to use novel approaches. However, efforts become irresponsible when the analysis is driven primarily by the desire to achieve certain results or enhance significance, rather than a drive to present the actual findings.</p>
							
							<h3>Summary</h3>
							
							<p>Researchers are expected to be creative and imaginative when they analyse their findings. At the same time, to ensure that you are adhering to the agreed standards in your field, you should:</p>
							
							<ul>
								<li>Know and follow best practice in your field of research</li>
								<li>Carefully and fully explain to others your analysis and interpretation process</li>
								<li>Let others know if there are reasons to question your findings.</li>
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