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Illustrates correlation and regression in food and consumer science applications, with worked examples of these methods on data from a consumer survey and from a sensory‐versus‐instrumental study.

Describes methods for examination of the relationship between two variables measured on a set of objects, using correlation coefficients and simple regression analysis.

Describes statistical methods applied to sensory discrimination tests. Illustrates binomial and chi‐square statistical analysis and discusses similarity testing, power and replication in discrimination testing.

Describes the application of correlation and regression in food science and nutrition studies.

While innovation has many similarities to other forms of projects it is characterised by a high failure rate and the need to stimulate creativity. More explicit risk management could help in achieving success in innovation projects. However, too much or inappropriate risk management might stifle the creativity that is core to innovation. So, what project risk management should be applied and where in the innovation project?

A theoretical framework is proposed which combines the generic innovation process with project risk management. The framework was used to analyse the current attitudes to managing innovation risk in a series of companies.

The decision points of the stage-gate innovation process model provide an effective interface for incorporating project risk concepts. The general concepts appear most relevant to innovation management though it is useful to customise them to emphasise the particular characteristics of innovation projects. The experience of using the resultant combined model in a number of diverse case studies indicates the relevance of the model in understanding attitudes towards risk management in innovation. The analysis of the case study companies suggested that risk management needs to be applied in differential manner: simple, unobtrusive techniques early in the innovation life cycle with more substantial, quantitative methods being considered for later stages.

It would be useful to extend this research by examining more case studies from other countries and industries.

The combined innovation and risk management model provides a framework that diverse companies can appreciate. The framework offers a basis for discussing the most appropriate form of risk management in different innovation-based industries.

Although there are many separate models for innovation and project risk management described in the literature, there is very little discussion about explicitly combining these theories. This paper aims to help fill this gap in the knowledge.

The purpose of this paper is to ascertain children's perception of fruit and fruit snacks and the influences on their choice.

One hundred primary school children (the majority aged 7‐11 years), from three schools, were surveyed or interviewed. A quota sample was taken with a balance of age and gender. A questionnaire survey (n = 50) plus a series of focus groups (n = 50) were carried out.

The questionnaire results showed that the children perceived fruit as likeable, healthy, convenient, low cost and available. Dried and packed fruits were of lower levels on these attributes but newer manufactured snacks were likeable and convenient, but viewed as unhealthy and costly. Focus groups revealed similar perceptions except in the case of fresh fruit which was seen as lacking convenience in terms of poor storage properties and waste.

The paper uses a convenience sample with no socio‐economic variation.

The paper offers new information on new fruit snack forms.

Introduces some simple statistics employed in analysing chemical analysis data. Describes measures of precision and accuracy and how the use of confidence intervals and repeatability can guide validation of data.

Begins an introduction to statistics for the food scientist. Aimed at the non‐mathematician, discusses the application of the statistical approach and introduces statistical measures for data presentation and summary.

Describes non‐parametric methods applied to comparisons of three or more sample groups.Illustrates non‐parametric ANOVA applied to a randomized block design for a consumer sensory experiment.

Describes multiple comparison procedures applied to three or more sample groups after ANOVA. Illustrates ANOVA applied to experiments with completely randomised design and randomised block design.